Na, Man Gyun; Oh, Seungrohk
2002-11-15
A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.
NASA Astrophysics Data System (ADS)
Shahbudin, S.; Hussain, A.; El-Shafie, Ahmed; Tahir, N. M.; Samad, S. A.
In this paper, a neuro-fuzzy technique known as the Adaptive-Neuro Fuzzy Inference System (ANFIS) has been used to highlight the application of ANFIS to perform human posture classification task using the new simplified shock graph (SSG) representation. Basically, a shock graph is a shape abstraction that decomposed a shape into a set of hierarchically organized primitive parts. The shock graph that represents the silhouette of an object in terms of a set of qualitatively defined parts and organized in a hierarchical, directed acyclic graph is used as a powerful representation of human shape in our work. The SSG feature provides a compact, unique and simple way of representing human shape and has been tested with several classifiers. As such, in this paper we intend to test its efficacy with another classifier, that is, the ANFIS classifier system. The result showed that the proposed ANFIS model can be used in classifying various human postures.
NASA Astrophysics Data System (ADS)
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi
2014-01-01
To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
Memristive Neuro-Fuzzy System.
Merrikh-Bayat, Farnood; Shouraki, Saeed Bagheri
2013-02-01
In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits. Simulation results confirm the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain. PMID:22851278
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665
Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping
NASA Astrophysics Data System (ADS)
Park, Inhye; Choi, Jaewon; Jin Lee, Moung; Lee, Saro
2012-11-01
We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.
Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)
NASA Astrophysics Data System (ADS)
Kakar, Manish; Nyström, Håkan; Rye Aarup, Lasse; Jakobi Nøttrup, Trine; Rune Olsen, Dag
2005-10-01
The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.
NASA Astrophysics Data System (ADS)
Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.
2011-04-01
Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.
Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.
Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise
2016-05-01
In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (V˙O2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment. PMID:26851475
Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
NASA Astrophysics Data System (ADS)
Valyrakis, Manousos; Zhang, Hanqing
2014-05-01
Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health of river systems but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation.
Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms
NASA Astrophysics Data System (ADS)
Chaudhuri, Sutapa; Middey, Anirban
2011-11-01
The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April-May) over Kolkata (22°32'N, 88°20'E), India. The pre-monsoon thunderstorms during 1997-2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12 h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.
NASA Astrophysics Data System (ADS)
Oğuz, Yüksel; Üstün, Seydi Vakkas; Yabanova, İsmail; Yumurtaci, Mehmet; Güney, İrfan
2012-01-01
This article presents design of adaptive neuro-fuzzy inference system (ANFIS) for the turbine speed control for purpose of improving the power quality of the power production system of a split shaft microturbine. To improve the operation performance of the microturbine power generation system (MTPGS) and to obtain the electrical output magnitudes in desired quality and value (terminal voltage, operation frequency, power drawn by consumer and production power), a controller depended on adaptive neuro-fuzzy inference system was designed. The MTPGS consists of the microturbine speed controller, a split shaft microturbine, cylindrical pole synchronous generator, excitation circuit and voltage regulator. Modeling of dynamic behavior of synchronous generator driver with a turbine and split shaft turbine was realized by using the Matlab/Simulink and SimPowerSystems in it. It is observed from the simulation results that with the microturbine speed control made with ANFIS, when the MTPGS is operated under various loading situations, the terminal voltage and frequency values of the system can be settled in desired operation values in a very short time without significant oscillation and electrical production power in desired quality can be obtained.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
NASA Astrophysics Data System (ADS)
Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan
2010-05-01
Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes. As a final conclusion, the results obtained from the study showed that the ANFIS modeling is a very useful and powerful tool for the regional landslide susceptibility assessments. However, the results to be obtained from the ANFIS modeling should be assessed carefully because the overlearning may cause misleading results. To prevent overlerning, the numbers of membership functions of inputs and the number of training epochs should be selected optimally and carefully.
NASA Astrophysics Data System (ADS)
Mahandrio, Irsantyo; Budi, Andriantama; Liong, The Houw; Purqon, Acep
2015-09-01
The growing patterns in cultural and mining sectors are interesting particularly in developed country such as in Indonesia. Here, we investigate the local characteristics of stocks between the sectors of agriculture and mining which si representing two leading companies and two common companies in these sectors. We analyze the prediction by using Adaptive Neuro Fuzzy Inference System (ANFIS). The type of Fuzzy Inference System (FIS) is Sugeno type with Generalized Bell membership function (Gbell). Our results show that ANFIS is a proper method to predicting the stock market with the RMSE : 0.14% for AALI and 0.093% for SGRO representing the agriculture sectors, meanwhile, 0.073% for ANTM and 0.1107% for MDCO representing the mining sectors.
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823
NASA Astrophysics Data System (ADS)
Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep
2015-09-01
Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy
Aqil, M; Kita, I; Yano, A; Nishiyama, S
2006-01-01
It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control. PMID:17302300
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811
Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
Jhin, Changho; Hwang, Keum Taek
2015-01-01
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167
NASA Astrophysics Data System (ADS)
Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad
2012-05-01
Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.
Torshabi, Ahmad Esmaili
2014-12-01
In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works. PMID:25412886
NASA Astrophysics Data System (ADS)
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2015-06-01
This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.
NASA Astrophysics Data System (ADS)
Heidary, Saeed; Setayeshi, Saeed
2015-01-01
This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous 99mTc/201Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of 201Tl (77±10% keV) and 99mTc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.
NASA Astrophysics Data System (ADS)
Goodarzi, Mohammad; Olivieri, Alejandro C.; Freitas, Matheus P.
2009-08-01
A spectrophotometric method for the simultaneous determination of Al(III), Co(II) and Ni(II) using Alizarin Red S as a chelating agent was developed. The parameters controlling the behavior of the system were investigated and optimum conditions were selected. The presence of non-linearities was checked using Mallows augmented partial residual plots. To take into account these non-linearities, a principal component analysis-adaptive neuro-fuzzy inference systems (PC-ANFISs) method was used for the analysis of ternary mixtures of Al(III), Co(II) and Ni(II) over the range of 0.05-0.90, 0.05-4.05 and 0.05-0.95 μg mL -1, respectively. Absorbance data were collected between 370 and 700 nm. The method was applied to accurately and simultaneously determines the content of metal ions in several synthetic mixtures.
NASA Astrophysics Data System (ADS)
Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.
2013-09-01
This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.
Incremental neuro-fuzzy systems
NASA Astrophysics Data System (ADS)
Fritzke, Bernd
1997-10-01
The poor scaling behavior of grid-partitioning fuzzy systems in case of increasing data dimensionality suggests using fuzzy systems with a scatter-partition of the input space. Jang has shown that zero-order Sugeno fuzzy systems are equivalent to radial basis function networks (RBFNs). Methods for finding scatter partitions for RBFNs are available, and it is possible to use them for creating scatter-partitioning fuzzy systems. A fundamental problem, however, is the structure identification problem, i.e., the determination of the number of fuzzy rules and their positions in the input space. The supervised growing neural gas method uses classification or regression error to guide insertions of new RBF units. This leads to a more effective positioning of RBF units (fuzzy rule IF-parts, resp.) than achievable with the commonly used unsupervised clustering methods. Example simulations of the new approach are shown demonstrating superior behavior compared with grid-partitioning fuzzy systems and the standard RBF approach of Moody and Darken.
NASA Astrophysics Data System (ADS)
Ajay Kumar, M.; Srikanth, N.
2014-03-01
In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.
Skin Cancer Recognition by Using a Neuro-Fuzzy System
Salah, Bareqa; Alshraideh, Mohammad; Beidas, Rasha; Hayajneh, Ferial
2011-01-01
Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%. PMID:21340020
NASA Astrophysics Data System (ADS)
Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo
2013-03-01
The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.
NASA Astrophysics Data System (ADS)
Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A. R.; Mazinani, M.
2015-04-01
Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency.
Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise
2015-09-01
This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration. PMID:25959320
Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F
2014-10-15
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. PMID:24858196
NASA Astrophysics Data System (ADS)
Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.
2015-12-01
This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.
NASA Astrophysics Data System (ADS)
Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.
2014-10-01
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (<22.46 Å) and average particle size lower than 48.8 Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.
NASA Astrophysics Data System (ADS)
Aminifar, S.; Yosefi, Gh.
2007-09-01
In this paper, we present away of using Anfis architecture to implement a new fuzzy logic controller chip. Anfis which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data makes fuzzy system to learn. This training is given from a standard response of the system and membership functions are suitably modified. For adaptive Anfis based fuzzy controller and its circuit design, we propose new circuits for implementing each controller block, and illustrate the test results and control surface of Anfis controller along with CMOS fuzzy logic controller using Matlab and Hspice software respectively. For implementing controller according to the Anfis training, we proposed new and improved integrated circuits which consist of Fuzzifier, Min operator and Multiplier/Divider. The control surfaces of controller are obtained by using Anfis training and simulation results of integrated circuits in less than 0.075 mm2 area in 0.35 ?m CMOS standard technology.
Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.
Ibrahim, Sulaimon; Chowriappa, Pradeep; Dua, Sumeet; Acharya, U Rajendra; Noronha, Kevin; Bhandary, Sulatha; Mugasa, Hatwib
2015-12-01
Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%. PMID:26109519
A Neuro-Fuzzy System for Characterization of Arm Movements
Balbinot, Alexandre; Favieiro, Gabriela
2013-01-01
The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours). PMID:23429579
System identification of smart structures using a wavelet neuro-fuzzy model
NASA Astrophysics Data System (ADS)
Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar
2012-11-01
This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.
Prognosis of machine health condition using neuro-fuzzy systems
NASA Astrophysics Data System (ADS)
Wang, Wilson Q.; Golnaraghi, M. Farid; Ismail, Fathy
2004-07-01
A reliable machine fault prognostic system can be used to forecast damage propagation trend in rotary machinery and to provide an alarm before a fault reaches critical levels. Currently, there are several techniques available in the literature for time-series prediction. Among the most promising methods are recurrent neural networks (RNNs) and neuro-fuzzy (NF) systems. In this paper, the performance of these two types of predictors is evaluated using two benchmark data sets. Through comparison it is found that if an NF system is properly trained, it performs better than RNNs in both forecasting accuracy and training efficiency. Accordingly, NF system is adopted to develop an on-line machine fault prognostic system. In order to facilitate the automatic monitoring process, reference function approach is proposed here to enhance feature representation. The performance of the developed prognostic system is evaluated by using three test cases including a worn gear, a chipped gear, and a cracked gear, as well as using data sets from previous studies corresponding to a gear pitting damage and a shaft misalignment. From these tests, the NF prognostic system is found to be a very reliable and robust machine health condition predictor. It can capture the system dynamic behaviour quickly and accurately.
NASA Astrophysics Data System (ADS)
Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad
2014-09-01
The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.
NASA Astrophysics Data System (ADS)
Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.
2016-02-01
All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
NASA Astrophysics Data System (ADS)
Subashini, L.; Vasudevan, M.
2012-02-01
Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.
A transductive neuro-fuzzy controller: application to a drilling process.
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
2010-07-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. PMID:20659865
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlović, Nenad T.; Akib, Shatirah
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to predict MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using MATLAB/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Simulation of elastic tissues in virtual medicine using neuro-fuzzy systems
NASA Astrophysics Data System (ADS)
Radetzky, Arne; Nuernberger, Andreas; Pretschner, Dietrich P.
1998-06-01
To improve the benefit of surgical simulators for education and research a visual convincing modeling of the operation scenario and the involved tissues is not sufficient. It is also necessary to simulate the deformation and resulting inner forces of tissue under influence of external forces caused by, for example, medical instruments or gravity. In this paper, we present a hybrid neuro-fuzzy system, which was designed for the description and simulation of tissues. The neuro-fuzzy system can be used to simulate the physical behavior like stiffness, viscosity and inertia of deformable or elastic tissues in surgical simulation. The parameters of a physical model or prior expert knowledge in the form of linguistic terms can be used to initialize the network parameters. Using a neural network structure, local changes to the system like cuts or ruptures can be performed during simulation. As an application example, some simulation results in the area of gynecological laparoscopy are given.
Simulink-based HW/SW codesign of embedded neuro-fuzzy systems.
Reyneri, L M; Chiaberge, M; Lavagno, L
2000-06-01
We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures. PMID:11011793
Inference of S-wave velocities from well logs using a Neuro-Fuzzy Logic (NFL) approach
NASA Astrophysics Data System (ADS)
Aldana, Milagrosa; Coronado, Ronal; Hurtado, Nuri
2010-05-01
The knowledge of S-wave velocity values is important for a complete characterization and understanding of reservoir rock properties. It could help in determining fracture propagation and also to improve porosity prediction (Cuddy and Glover, 2002). Nevertheless the acquisition of S-wave velocity data is rather expensive; hence, for most reservoirs usually this information is not available. In the present work we applied a hybrid system, that combines Neural Networks and Fuzzy Logic, in order to infer S-wave velocities from porosity (φ), water saturation (Sw) and shale content (Vsh) logs. The Neuro-Fuzzy Logic (NFL) technique was tested in two wells from the Guafita oil field, Apure Basin, Venezuela. We have trained the system using 50% of the data randomly taken from one of the wells, in order to obtain the inference equations (Takani-Sugeno-Kang (TSK) fuzzy model). Equations using just one of the parameters as input (i.e. φ, Sw or Vsh), combined by pairs and all together were obtained. These equations were tested in the whole well. The results indicate that the best inference (correlation between inferred and experimental data close to 80%) is obtained when all the parameters are considered as input data. An increase of the equation number of the TSK model, when one or just two parameters are used, does not improve the performance of the NFL. The best set of equations was tested in a nearby well. The results suggest that the large difference in the petrophysical and lithological characteristics between these two wells, avoid a good inference of S-wave velocities in the tested well and allowed us to analyze the limitations of the method.
NASA Astrophysics Data System (ADS)
Yang, G.; Lin, Y.; Bhattacharya, P.
2007-12-01
To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.
NASA Astrophysics Data System (ADS)
Ibrahim, Wael Refaat Anis
The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-01-01
The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications. PMID:25426427
Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control
NASA Astrophysics Data System (ADS)
Petrovic-Lazarevic, Sonja; Zhang, Jian Ying
2007-12-01
The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.
Tunnel stability analysis during construction using a neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Rangel, José Luis; Iturrarán-Viveros, Ursula; Ayala, A. Gustavo; Cervantes, Francisco
2005-12-01
This paper presents an alternative strategy to evaluate the stability of tunnels during the design and construction stages based on a hybrid system, composed by neural, neuro-fuzzy and analytical solutions. A prototype of this system is designed using a database formed by 261 cases, 45 real and the rest synthetic. This system is capable of reproducing the displacements induced at the periphery of the tunnel before and after support installation. The stability of the excavation process is evaluated using a criterion that considers dimensionless parameters based on the shear strength of the media, the induced deformation level in the ground, the plastic radii and the advance of excavation without support. The efficiency and validity of the prototype is verified with two examples of actual tunnels, one included in the database used to train the system and the other not included. The results of both examples show a better approximation than other commonly used techniques. Copyright
FPGA implementation of neuro-fuzzy system with improved PSO learning.
Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali
2016-07-01
This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources. PMID:27136666
A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors
Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José
2009-01-01
In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160
A neuro-fuzzy system for extracting environment features based on ultrasonic sensors.
Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José
2009-01-01
In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160
Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system
NASA Astrophysics Data System (ADS)
Pourmahyabadi, M.; Mohammad Nejad, S.
2009-10-01
The most common methods applied in the analysis of photonic crystal fibers (PCFs) are finite difference time/frequency domain (FDTD/FDFD) method and finite element method (FEM). These methods are very general and reliable (well tested). They describe arbitrary structure but are numerically intensive and require detailed treatment of boundaries and complex definition of calculation mesh. So these conventional models that simulate the photonic response of PCFs are computationally expensive and time consuming. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time. In this article, an artificial intelligence method such as Neuro-Fuzzy system is used to establish a model that can predict the properties of PCFs. Simulation results show that this model is remarkably effective in predicting the properties of PCF such as dispersion, dispersion slope and loss over the C communication band.
Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design
Cheng, Yi-Chang; Hsu, Yung-Chi
2010-01-01
In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations. PMID:21709856
Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions. PMID:26001214
Almonacid, Miguel; Cano-Izquierdo, José M.; Sabater-Navarro, José M.; Fernández, Eduardo
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions. PMID:26001214
NASA Astrophysics Data System (ADS)
Bazzazi, Abbas Aghajani; Esmaeili, Mohammad
2012-12-01
Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (
A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system
Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken
2010-12-15
This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)
NASA Astrophysics Data System (ADS)
Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok
2015-01-01
This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
NASA Technical Reports Server (NTRS)
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
Neuro-fuzzy and neural network systems for air quality control
NASA Astrophysics Data System (ADS)
Carnevale, Claudio; Finzi, Giovanna; Pisoni, Enrico; Volta, Marialuisa
In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source-receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source-receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source-receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source-receptor models are able to accurately reproduce the simulation of the 3D modelling system.
Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.
Arthi, K; Tamilarasi, A
2008-11-01
The major challenge in medical field is to diagnose disorder rather than a disease. In this paper, a neuro fuzzy based model is designed for identification or diagnosis of autism. The problematic areas are gathered from every individual and the related linguistic inputs are converted into fuzzy input values which are in turn given as input to feed forward multilayer neural network. The network is trained using back propagation training algorithm and tested for its performance with the expertise. PMID:18706991
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
NASA Astrophysics Data System (ADS)
Hurtado, N.; López, D.; Costanzo-Alvarez, V.; Aldana, M.
2009-04-01
We have measured NRM, room temperature magnetic susceptibility, S ratios and Königsberger ratios in 134 samples that encompass aproximately 670 meters of depth in an oil well drilled in eastern Colombia. These samples are sandstones and siltstones from the Guayabo, León and Carbonera Formations (Oligocene/Miocene/Pliocene). Our main goal is to asses the potential of the Neuro Fuzzy Logic analysis to infer magnetic parameters such as S ratios and Königsberger ratios from magnetic susceptibility experimental data. This method has been previously used with some success to obtain other petrophysical properties such as permeability out of porosity experimental data, however this is the first time it is applied to bulk magnetic properties. The results obtained here are then compared and integrated with their experimental counterparts. They are also used to study the variability of the paleoenvironmental conditions during the formation of the Barinas Apure sedimentary basin in eastern Colombia and western Venezuela.
Approximation abilities of neuro-fuzzy networks
NASA Astrophysics Data System (ADS)
Mrówczyńska, Maria
2010-01-01
The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.
A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Zeinali, Mohammadjavad; Amri Mazlan, Saiful; Yasser Abd Fatah, Abdul; Zamzuri, Hairi
2013-12-01
A magnetorheological (MR) damper is a promising appliance for semi-active suspension systems, due to its capability of damping undesired movement using an adequate control strategy. This research has been carried out a phenomenological dynamic model of two MR dampers using an adaptive-network-based fuzzy inference system (ANFIS) approach. Two kinds of Lord Corporation MR damper (a long stroke damper and a short stroke damper) were used in experiments, and then modeled using the experimental results. In addition, an investigation of the influence of the membership function selection on predicting the behavior of the MR damper and obtaining a mathematical model was conducted to realize the relationship between input current, displacement, and velocity as the inputs and force as output. The results demonstrate that the proposed models for both short stroke and long stroke MR dampers have successfully predicted the behavior of the MR damper with adequate accuracy, and an equation is presented to precisely describe the behavior of each MR damper.
NASA Astrophysics Data System (ADS)
Prakash, S.; Sinha, S. K.
2015-09-01
In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.
A neuro-fuzzy identification of ECG beats.
Chikh, Mohammed Amine; Ammar, Mohammed; Marouf, Radja
2012-04-01
This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level. PMID:20703643
Use of an adaptive neuro-fuzzy system to characterize root distribution patterns
Technology Transfer Automated Retrieval System (TEKTRAN)
Root-soil relationships are pivotal to understanding crop growth and function in a changing environmental. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statist...
Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems
NASA Technical Reports Server (NTRS)
Gulati, Sandeep; Toomarian, Nikzad; Barhen, Jacob; Maccalla, Ayanna; Tawel, Raoul; Thakoor, Anil; Daud, Taher
1994-01-01
Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.
Prediction of Conductivity by Adaptive Neuro-Fuzzy Model
Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.
Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla
2014-12-01
This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies. PMID:25406641
NASA Astrophysics Data System (ADS)
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach.
Ang, Kai Keng; Quek, Chai
2006-09-01
This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model. PMID:17001989
Landslide susceptibility mapping using a neuro-fuzzy
NASA Astrophysics Data System (ADS)
Lee, S.; Choi, J.; Oh, H.
2009-12-01
This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide location for generalization and prediction. It is easy to understand and interpret, therefore it is a good choice for modeling landslide susceptibility mapping, which are also of great help for planners and engineers in selecting highly susceptible areas for further detail surveys and suitable locations to implement development. Although they may be less useful at the site-specific scale, where local geological and geographic heterogeneities may prevail, the results herein may be used as basic data to assist slope management and land use planning. For the method to be more generally applied, more landslide data are needed and more case studies should be conducted.
Estimating the crowding level with a neuro-fuzzy classifier
NASA Astrophysics Data System (ADS)
Boninsegna, Massimo; Coianiz, Tarcisio; Trentin, Edmondo
1997-07-01
This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.
Daily soil temperature modeling using neuro-fuzzy approach
NASA Astrophysics Data System (ADS)
Hosseinzadeh Talaee, P.
2014-11-01
Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models' performances at arid and semiarid locations showed that the CANFIS models' performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.
Neuro-Fuzzy Phasing of Segmented Mirrors
NASA Technical Reports Server (NTRS)
Olivier, Philip D.
1999-01-01
A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
Recognition of Handwritten Arabic words using a neuro-fuzzy network
NASA Astrophysics Data System (ADS)
Boukharouba, Abdelhak; Bennia, Abdelhak
2008-06-01
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
Recognition of Handwritten Arabic words using a neuro-fuzzy network
Boukharouba, Abdelhak; Bennia, Abdelhak
2008-06-12
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
Motamedi, Shervin; Roy, Chandrabhushan; Shamshirband, Shahaboddin; Hashim, Roslan; Petković, Dalibor; Song, Ki-Il
2015-08-01
Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies. PMID:25957464
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
NASA Astrophysics Data System (ADS)
Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata
2014-12-01
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.
Neuro-Fuzzy Control of a Robotic Manipulator
NASA Astrophysics Data System (ADS)
Gierlak, P.; Muszyńska, M.; Żylski, W.
2014-08-01
In this paper, to solve the problem of control of a robotic manipulator's movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator's dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator
Prediction of contact forces of underactuated finger by adaptive neuro fuzzy approach
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Shamshirband, Shahaboddin; Abbasi, Almas; Kiani, Kourosh; Al-Shammari, Eiman Tamah
2015-12-01
To obtain adaptive finger passive underactuation can be used. Underactuation principle can be used to adapt shapes of the fingers for grasping objects. The fingers with underactuation do not require control algorithm. In this study a kinetostatic model of the underactuated finger mechanism was analyzed. The underactuation is achieved by adding the compliance in every finger joint. Since the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. In this study prediction of the contact forces was established by a soft computing approach. Adaptive neuro-fuzzy inference system (ANFIS) was applied as the soft computing method to perform the prediction of the finger contact forces.
Potential of neuro-fuzzy methodology to estimate noise level of wind turbines
NASA Astrophysics Data System (ADS)
Nikolić, Vlastimir; Petković, Dalibor; Por, Lip Yee; Shamshirband, Shahaboddin; Zamani, Mazdak; Ćojbašić, Žarko; Motamedi, Shervin
2016-01-01
Wind turbines noise effect became large problem because of increasing of wind farms numbers since renewable energy becomes the most influential energy sources. However, wind turbine noise generation and propagation is not understandable in all aspects. Mechanical noise of wind turbines can be ignored since aerodynamic noise of wind turbine blades is the main source of the noise generation. Numerical simulations of the noise effects of the wind turbine can be very challenging task. Therefore in this article soft computing method is used to evaluate noise level of wind turbines. The main goal of the study is to estimate wind turbine noise in regard of wind speed at different heights and for different sound frequency. Adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the wind turbine noise levels.
NASA Astrophysics Data System (ADS)
Setia, Ronald; May, Gary S.
2006-02-01
Excimer laser ablation is used for microvia formation in the microelectronics packaging industry. With continuing advancement of laser systems, there is an increasing need to offset capital equipment investment and lower equipment downtime. This paper presents a neuro-fuzzy methodology for in-line failure detection and diagnosis of the excimer laser ablation process. Response data originating directly from laser tool sensors and the characterization of microvias were used as failure symptoms for potential deviations in four laser system parameters from their corresponding baseline values. The response characteristics consist of via diameter, via wall angle, and via resistance. Resistance measurements on copper deposited in the ablated vias were performed to characterize the degree to which debris remaining inside the vias affected quality. The laser system parameters include laser fluence, shot frequency, number of pulses, and helium pressure flow. The adaptive neuro-fuzzy inference system (ANFIS) was trained and subsequently validated for its capability in evidential reasoning using the data collected. Results indicated only a single false alarm occurred in 19 possible failure detection scenarios. In failure diagnosis, a single false alarm and a single missed alarm occurred.
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred
Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86
Spatial Disorder in Complex Neuro-Fuzzy Dynamics
NASA Astrophysics Data System (ADS)
Bucolo, M.; Fortuna, L.
In this paper one-dimensional chains of locally interconnected nonlinear dynamical systems are considered, focusing on how to reach a self-organised behaviour by introducing spatial disorder. In particular each sub-system in the chain is modelled by using Neuro-Fuzzy technique which allows us to create very complex systems employing both empirical and measured quantities. The possibility to reach spatio-temporal organisation is investigated through experiments in which uncertainty is introduced in the parameters of each local sub-system. The obtained results are very interesting, since they address the use of disorder as a suitable way to control spatio-temporal irregular behaviour in a chain of fuzzy systems.
Neuro-fuzzy models in pattern recognition
NASA Astrophysics Data System (ADS)
Mitra, Sunanda; Kim, Yong Soo
1993-12-01
Research in the last decade emphasized the potential of designing adaptive pattern recognition classifiers based on algorithms using multi-layered artificial neural nets. The greatest potential in such endeavors was anticipated to be not only in the adaptivity but also in the high-speed processing through massively parallel VLSI implementation and optical computing. Computational advantages of such algorithms have been demonstrated in a number of papers. Neural networks particularly the self-organizing types have been found quite suitable crisp pattern for clustering of unlabeled datasets. The generalization of Kohonen-type learning vector quantization (LVQ) clustering algorithm to fuzzy LVQ clustering algorithm and its equivalence to fuzzy c-means has been clearly demonstrated recently. On the other hand, Carpenter/Grossberg's ART-type self organizing neural networks have been modified to perform fuzzy clustering by a number of researches in the past few years. The performance of such neuro-fuzzy models in clustering unlabeled data patterns is addressed in this paper. A recent development of a new similarity measure and a new learning rule for updating the centroid of the winning cluster in a fuzzy ART-type neural network is also described. The capability of the above neuro-fuzzy model in better partitioning of datasets into clusters of any shape is demonstrated.
NASA Astrophysics Data System (ADS)
Al-Shammari, Eiman Tamah; Petković, Dalibor; Danesh, Amir Seyed; Shamshirband, Shahaboddin; Issa, Mirna; Zentner, Lena
2016-05-01
Robotic operations need to be safe for unpredictable contacts. Joints with passive compliance with springs can be used for soft robotic contacts. However the joints cannot measure external collision forces. In this investigation was developed one passive compliant joint which have soft contacts with external objects and measurement capabilities. To ensure it, conductive silicone rubber was used as material for modeling of the compliant segments of the robotic joint. These compliant segments represent embedded sensors. The conductive silicone rubber is electrically conductive by deformations. The main task was to obtain elastic absorbers for the external collision forces. These absorbers can be used for measurement in the same time. In other words, the joint has an internal measurement system. Adaptive neuro fuzzy inference system (ANFIS) was used to estimate the safety level of the robotic joint by head injury criteria (HIC).
NASA Astrophysics Data System (ADS)
Dixon, B.
2005-07-01
Modeling groundwater vulnerability reliably and cost effectively for non-point source (NPS) pollution at a regional scale remains a major challenge. In recent years, Geographic Information Systems (GIS), neural networks and fuzzy logic techniques have been used in several hydrological studies. However, few of these research studies have undertaken an extensive sensitivity analysis. The overall objective of this research is to examine the sensitivity of neuro-fuzzy models used to predict groundwater vulnerability in a spatial context by integrating GIS and neuro-fuzzy techniques. The specific objectives are to assess the sensitivity of neuro-fuzzy models in a spatial domain using GIS by varying (i) shape of the fuzzy sets, (ii) number of fuzzy sets, and (iii) learning and validation parameters (including rule weights). The neuro-fuzzy models were developed using NEFCLASS-J software on a JAVA platform and were loosely integrated with a GIS. Four plausible parameters which are critical in transporting contaminants through the soil profile to the groundwater, included soil hydrologic group, depth of the soil profile, soil structure (pedality points) of the A horizon, and landuse. In order to validate the model predictions, coincidence reports were generated among model inputs, model predictions, and well/spring contamination data for NO 3-N. A total of 16 neuro-fuzzy models were developed for selected sub-basins of Illinois River Watershed, AR. The sensitivity analysis showed that neuro-fuzzy models were sensitive to the shape of the fuzzy sets, number of fuzzy sets, nature of the rule weights, and validation techniques used during the learning processes. Compared to bell-shaped and triangular-shaped membership functions, the neuro-fuzzy models with a trapezoidal membership function were the least sensitive to the various permutations and combinations of the learning and validation parameters. Over all, Models 11 and 8 showed relatively higher coincidence with well contamination data than other models. The strength of this method is that it offers a means of dealing with imprecise data, therefore, is a viable option for regional and continental scale environmental modeling where imprecise data prevail. The neuro-fuzzy models, however, should only be used as a tool within a broader framework of GIS, remote sensing and solute transport modeling to assess groundwater vulnerability along with functional, mechanistic and stochastic models.
NASA Astrophysics Data System (ADS)
El-Sebakhy, Emad A.
2009-09-01
Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.
Comparative analysis of fuzzy inference systems for water consumption time series prediction
NASA Astrophysics Data System (ADS)
Firat, Mahmut; Turan, Mustafa Erkan; Yurdusev, Mehmet Ali
2009-08-01
SummaryTwo types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series.
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin
2014-01-01
Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat
2016-05-01
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.
Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine
Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin
2014-01-01
Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621
Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.
Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad
2013-05-01
In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model. PMID:23111771
Predictive neuro-fuzzy controller for multilink robot manipulator
NASA Astrophysics Data System (ADS)
Kaymaz, Emre; Mitra, Sunanda
1995-10-01
A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems including multilink robot manipulators. The proposed controller is particularly useful when the dynamics of the nonlinear system to be controlled are difficult to yield exact solutions and the system specification can be obtained in terms of crisp input-output pairs. It inherits the advantages of both fuzzy logic and predictive control. The identification of the nonlinear mapping of the system to be controlled is realized by a three- layer feed-forward neural network model employing the input-output data obtained from the system. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as a simulation tool to generate the input-output data for developing the predictive fuzzy logic controller for the chosen nonlinear system. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the input and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, it is not necessary to tune the controller. For a two-link robot manipulator, the performance of this predictive fuzzy controller is shown to be superior to that of a conventional controller employing an ARMA model of the system in terms of accuracy and consumption of energy.
Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia
NASA Astrophysics Data System (ADS)
Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg
2013-03-01
Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.
NASA Astrophysics Data System (ADS)
Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng
2016-05-01
Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.
NASA Astrophysics Data System (ADS)
Lin, J.; Zheng, Y. B.
2012-07-01
The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.
Neuro-Fuzzy Support of Knowledge Management in Social Regulation
NASA Astrophysics Data System (ADS)
Petrovic-Lazarevic, Sonja; Coghill, Ken; Abraham, Ajith
2002-09-01
The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on social policies and procedures. Tacit knowledge is ethics and norms of the community. The former could be codified, stored and transferable in order to support decision making, while the latter being based on personal knowledge, experience and judgments is difficult to codify and store. Tacit knowledge expressed through linguistic information can be stored and used to support knowledge management in social regulation through the application of fuzzy and neuro-fuzzy logic.
Wang, Yu; Winters, Jack M
2005-01-01
Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs). Methods The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. Results The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. Conclusion A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective. PMID:15985181
Mahersia, Hela; Boulehmi, Hela; Hamrouni, Kamel
2016-04-01
Female breast cancer is the second most common cancer in the world. Several efforts in artificial intelligence have been made to help improving the diagnostic accuracy at earlier stages. However, the identification of breast abnormalities, like masses, on mammographic images is not a trivial task, especially for dense breasts. In this paper we describe our novel mass detection process that includes three successive steps of enhancement, characterization and classification. The proposed enhancement system is based mainly on the analysis of the breast texture. First of all, a filtering step with morphological operators and soft thresholding is achieved. Then, we remove from the filtered breast region, all the details that may interfere with the eventual masses, including pectoral muscle and galactophorous tree. The pixels belonging to this tree will be interpolated and replaced by the average of the neighborhood. In the characterization process, measurement of the Gaussian density in the wavelet domain allows the segmentation of the masses. Finally, a comparative classification mechanism based on the Bayesian regularization back-propagation networks and ANFIS techniques is proposed. The tests were conducted on the MIAS database. The results showed the robustness of the proposed enhancement method. PMID:26831269
Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter
2013-02-01
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047
Shamshirband, Shahaboddin; Banjanovic-Mehmedovic, Lejla; Bosankic, Ivan; Kasapovic, Suad; Abdul Wahab, Ainuddin Wahid Bin
2016-01-01
Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder. PMID:27219539
NASA Astrophysics Data System (ADS)
Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.
2012-04-01
Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.
Backpropagation through time training of a neuro-fuzzy controller.
Koprinkova-Hristova, Petia
2010-10-01
The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too. PMID:20945520
NASA Astrophysics Data System (ADS)
Vaganova, E. V.; Syryamkin, M. V.
2015-11-01
The purpose of the research is the development of evolutionary algorithms for assessments of promising scientific directions. The main attention of the present study is paid to the evaluation of the foresight possibilities for identification of technological peaks and emerging technologies in professional medical equipment engineering in Russia and worldwide on the basis of intellectual property items and neural network modeling. An automated information system consisting of modules implementing various classification methods for accuracy of the forecast improvement and the algorithm of construction of neuro-fuzzy decision tree have been developed. According to the study result, modern trends in this field will focus on personalized smart devices, telemedicine, bio monitoring, «e-Health» and «m-Health» technologies.
NASA Astrophysics Data System (ADS)
Oh, Hyun-Joo; Pradhan, Biswajeet
2011-09-01
This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.
NASA Astrophysics Data System (ADS)
Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi
2007-04-01
SummaryModeling of rainfall-runoff dynamics is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity for calibrating the nonlinear relationships inherent in the rainfall-runoff process. In this study, the advantages of artificial neural networks and neuro-fuzzy system in continuous modeling of the daily and hourly behaviour of runoff were examined. Three different adaptive techniques were constructed and examined namely, Levenberg-Marquardt feed forward neural network, Bayesian regularization feed forward neural network, and neuro-fuzzy. In addition, the effects of data transformation on model performance were also investigated. This was done by examining the performance of the three network architectures and training algorithms using both raw and transformed data. Through inspection of the results it was found that although the model built on transformed data outperforms the model built on raw data, no significant differences were found between the forecast accuracies of the three examined models. A detailed comparison of the overall performance indicated that the neuro-fuzzy model performed better than both the Levenberg-Marquardt-FFNN and the Bayesian regularization-FFNN. In order to enable users to process the data easily, a graphic user interface (GUI) was developed. This program allows users to process the rainfall-runoff data, to train/test the model using various input options and to visualize results.
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
NASA Technical Reports Server (NTRS)
Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza
2003-01-01
Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.
Neuro-fuzzy based model of batch fermentation of Kluyveromyces marxianus var. lactis MC5
Ilkova, Tatiana; Petrov, Mitko
2014-01-01
In this work a neuro-fuzzy based model of a whey batch fermentation process by a strain Kluyveromyces marxianus var. lactis MC5 is presented. A three-layered neuro-fuzzy network is realized. The simulation results are compared with conventional models (based on mass balance and differential equations). The neuro-fuzzy model provides a better fitness and allows inclusion of linguistic variables (such as colour, smell, taste, morphophysiology, etc.). The accuracy is approximately equal to this achieved by a conventional neural network. The proposed approach is flexible (with regard to the process model) and quite robust (with regard to the possible uncertainties and to the optimization surface). Future work will focus on applying this approach for modelling of different biotechnological processes. PMID:26019585
Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers
NASA Astrophysics Data System (ADS)
Kim, Sung-Woo; Park, Sang-Young; Park, Chandeok
2016-01-01
In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.
A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance
2013-01-01
Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. PMID:24302928
Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease
2015-01-01
Background This paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network. Methods Fuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo. Results Out of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained. Conclusion Our neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification. Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patient's dynamic assessment, as opposed to the solution that provides a static assessment of the patient. PMID:26391218
Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.
Carrascal, A; Manrique, D; Ríos, J; Rossi, C
2003-01-01
This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach. PMID:14629866
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. PMID:25453384
Neuro-fuzzy based time-delay estimation using DCT coefficients.
Shaltaf, Samir
2007-02-01
In this research, a neuro-fuzzy system (NFS) is introduced into the problem of time-delay estimation. Time-delay estimation deals with the problem of estimating a constant time delay embedded within a received noisy and delayed replica of a known reference signal. The received signal is filtered and discrete cosine transformed into DCT coefficients. The time delay is now encoded into the DCT coefficients. Only those few DCT coefficients which possess high sensitivity to time-delay variations are used as input to the NFS. The NFS is used for time-delay estimation because of its ability of learning highly nonlinear relationships and encoding them into its internal structure. This capability is used in learning the nonlinear relationship between the DCT coefficients of a delayed reference signal and the time delay embedded into this signal. The NFS is trained with several hundred training sets in which the highly sensitive DCT coefficients were applied as input, and the corresponding time delay was the output. In the testing phase, DCT coefficients of delayed signals were applied as input to the NFS and the system produced accurate time-delay estimates, as compared to those obtained by the classical cross-correlation technique. PMID:17266957
Julie, E Golden; Selvi, S Tamil
2016-01-01
Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269
Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1996-01-01
Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.
Julie, E. Golden; Selvi, S. Tamil
2016-01-01
Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269
Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning
NASA Astrophysics Data System (ADS)
Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik
2013-04-01
SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.
Cheu, Eng Yeow; Quek, Chai; Ng, See Kiong
2012-02-01
Appetitive operant conditioning in Aplysia for feeding behavior via the electrical stimulation of the esophageal nerve contingently reinforces each spontaneous bite during the feeding process. This results in the acquisition of operant memory by the contingently reinforced animals. Analysis of the cellular and molecular mechanisms of the feeding motor circuitry revealed that activity-dependent neuronal modulation occurs at the interneurons that mediate feeding behaviors. This provides evidence that interneurons are possible loci of plasticity and constitute another mechanism for memory storage in addition to memory storage attributed to activity-dependent synaptic plasticity. In this paper, an associative ambiguity correction-based neuro-fuzzy network, called appetitive reward-based pseudo-outer-product-compositional rule of inference [ARPOP-CRI(S)], is trained based on an appetitive reward-based learning algorithm which is biologically inspired by the appetitive operant conditioning of the feeding behavior in Aplysia. A variant of the Hebbian learning rule called Hebbian concomitant learning is proposed as the building block in the neuro-fuzzy network learning algorithm. The proposed algorithm possesses the distinguishing features of the sequential learning algorithm. In addition, the proposed ARPOP-CRI(S) neuro-fuzzy system encodes fuzzy knowledge in the form of linguistic rules that satisfies the semantic criteria for low-level fuzzy model interpretability. ARPOP-CRI(S) is evaluated and compared against other modeling techniques using benchmark time-series datasets. Experimental results are encouraging and show that ARPOP-CRI(S) is a viable modeling technique for time-variant problem domains. PMID:24808510
NASA Astrophysics Data System (ADS)
Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.
2012-04-01
The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.
NASA Astrophysics Data System (ADS)
Shiri, Jalal; Nazemi, Amir Hossein; Sadraddini, Ali Ashraf; Landeras, Gorka; Kisi, Ozgur; Fard, Ahmad Fakheri; Marti, Pau
2013-02-01
SummaryAccurate estimation of reference evapotranspiration is important for irrigation scheduling, water resources management and planning and other agricultural water management issues. In the present paper, the capabilities of generalized neuro-fuzzy models were evaluated for estimating reference evapotranspiration using two separate sets of weather data from humid and non-humid regions of Spain and Iran. In this way, the data from some weather stations in the Basque Country and Valencia region (Spain) were used for training the neuro-fuzzy models [in humid and non-humid regions, respectively] and subsequently, the data from these regions were pooled to evaluate the generalization capability of a general neuro-fuzzy model in humid and non-humid regions. The developed models were tested in stations of Iran, located in humid and non-humid regions. The obtained results showed the capabilities of generalized neuro-fuzzy model in estimating reference evapotranspiration in different climatic zones. Global GNF models calibrated using both non-humid and humid data were found to successfully estimate ET0 in both non-humid and humid regions of Iran (the lowest MAE values are about 0.23 mm for non-humid Iranian regions and 0.12 mm for humid regions). non-humid GNF models calibrated using non-humid data performed much better than the humid GNF models calibrated using humid data in non-humid region while the humid GNF model gave better estimates in humid region.
NASA Astrophysics Data System (ADS)
Baraldi, Andrea; Binaghi, Elisabetta; Blonda, Palma N.; Brivio, Pietro A.; Rampini, Anna
1998-10-01
Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.
NASA Astrophysics Data System (ADS)
Hussain Mutlag, Ammar; Mohamed, Azah; Shareef, Hussain
2016-03-01
Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.
Streamflow Forecasting Using Nuero-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Nanduri, U. V.; Swain, P. C.
2005-12-01
The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.
Chen, Tien-Chi; Yu, Chih-Hsien; Chen, Chun-Jung; Tsai, Mi-Ching
2008-07-01
This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system. PMID:18501903
NASA Astrophysics Data System (ADS)
Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.
2009-08-01
In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.
Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang
2016-01-01
In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098
Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang
2016-01-01
In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider's hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse's gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider's motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country's top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098
Reactive navigation for autonomous guided vehicle using neuro-fuzzy techniques
NASA Astrophysics Data System (ADS)
Cao, Jin; Liao, Xiaoqun; Hall, Ernest L.
1999-08-01
A Neuro-fuzzy control method for navigation of an Autonomous Guided Vehicle robot is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by as terrain and a set of distinct objects, such as obstacles and landmarks. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Neural network and fuzzy logic control techniques can improve real-time control performance for mobile robot due to its high robustness and error-tolerance ability. For a mobile robot to navigate automatically and rapidly, an important factor is to identify and classify mobile robots' currently perceptual environment. In this paper, a new approach of the current perceptual environment feature identification and classification, which are based on the analysis of the classifying neural network and the Neuro- fuzzy algorithm, is presented. The significance of this work lies in the development of a new method for mobile robot navigation.
Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed
NASA Technical Reports Server (NTRS)
Olivier, Philip D.
2002-01-01
NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.
Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed
NASA Astrophysics Data System (ADS)
Olivier, Philip D.
2002-07-01
NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet
2013-02-01
The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.
Characterizing root distribution with adaptive neuro-fuzzy analysis
Technology Transfer Automated Retrieval System (TEKTRAN)
Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics...
A neuro-fuzzy architecture for real-time applications
NASA Technical Reports Server (NTRS)
Ramamoorthy, P. A.; Huang, Song
1992-01-01
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.
Adaptive neuro-fuzzy fusion of sensor data
NASA Astrophysics Data System (ADS)
Petković, Dalibor
2014-11-01
A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.
Ghorbanzadeh, Leila; Torshabi, Ahmad Esmaili; Nabipour, Jamshid Soltani; Arbatan, Moslem Ahmadi
2016-04-01
In image guided radiotherapy, in order to reach a prescribed uniform dose in dynamic tumors at thorax region while minimizing the amount of additional dose received by the surrounding healthy tissues, tumor motion must be tracked in real-time. Several correlation models have been proposed in recent years to provide tumor position information as a function of time in radiotherapy with external surrogates. However, developing an accurate correlation model is still a challenge. In this study, we proposed an adaptive neuro-fuzzy based correlation model that employs several data clustering algorithms for antecedent parameters construction to avoid over-fitting and to achieve an appropriate performance in tumor motion tracking compared with the conventional models. To begin, a comparative assessment is done between seven nuero-fuzzy correlation models each constructed using a unique data clustering algorithm. Then, each of the constructed models are combined within an adaptive sevenfold synthetic model since our tumor motion database has high degrees of variability and that each model has its intrinsic properties at motion tracking. In the proposed sevenfold synthetic model, best model is selected adaptively at pre-treatment. The model also updates the steps for each patient using an automatic model selectivity subroutine. We tested the efficacy of the proposed synthetic model on twenty patients (divided equally into two control and worst groups) treated with CyberKnife synchrony system. Compared to Cyberknife model, the proposed synthetic model resulted in 61.2% and 49.3% reduction in tumor tracking error in worst and control group, respectively. These results suggest that the proposed model selection program in our synthetic neuro-fuzzy model can significantly reduce tumor tracking errors. Numerical assessments confirmed that the proposed synthetic model is able to track tumor motion in real time with high accuracy during treatment. PMID:25765021
Verifying Stability of Dynamic Soft-Computing Systems
NASA Technical Reports Server (NTRS)
Wen, Wu; Napolitano, Marcello; Callahan, John
1997-01-01
Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.
NASA Astrophysics Data System (ADS)
Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok
2015-05-01
This work presents a novel neuro-fuzzy controller (NFC) for car-driver's seat-suspension system featuring magnetorheological (MR) dampers. The NFC is built based on the algorithm for building adaptive neuro-fuzzy inference systems (ANFISs) named B-ANFIS, which has been developed in Part 1, and fuzzy logic inference systems (FISs). In order to create the NFC, the following steps are performed. Firstly, a control strategy based on a ride-comfort-oriented tendency (RCOT) is established. Subsequently, optimal FISs are built based on a genetic algorithm (GA) to estimate the desired damping force that satisfies the RCOT corresponding to the road status at each time. The B-ANFIS is then used to build ANFISs for inverse dynamic models of the suspension system (I-ANFIS). Based on the FISs, the desired force values are calculated according to the status of road at each time. The corresponding exciting current value to be applied to the MR damper is then determined by the I-ANFIS. In order to validate the effectiveness of the developed neuro-fuzzy controller, control performances of the seat-suspension systems featuring MR dampers are evaluated under different road conditions. In addition, a comparative work between conventional skyhook controller and the proposed NFC is undertaken in order to demonstrate superior control performances of the proposed methodology.
Neuro fuzzy force control for soft dry contact Hertzian ultrasonic probe
NASA Astrophysics Data System (ADS)
Gallegos, E.; Baltazar, A.; Treesatayapun, C.
2016-02-01
In this work the use of a cartesian robotic manipulator as scanner for the automated identification of hidden defects in an aluminum test plate is proposed. The robotic manipulator includes a custom made soft deformable ultrasonic probe and a force sensor for the recollection of the ultrasonic signals and force feedback. The contact between the soft probe and the test plate is regulated using a Neuro Fuzzy controller in order to avoid the complex mathematical model produced by the interaction. Finally the use of the correlation coefficient is proposed for the post processing of the obtained ultrasonic signals and identification of hidden defects inside the test plate. Experimental studies demonstrated the efficiency of the method.
Kher, Rahul; Pawar, Tanmay; Thakar, Vishvjit; Shah, Hitesh
2015-02-01
The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects. PMID:25641014
Renjith, Arokia; Manjula, P; Mohan Kumar, P
2015-01-01
Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method. PMID:26493726
NASA Astrophysics Data System (ADS)
Hsu, Chi-Yao; Cheng, Yi-Chang; Lin, Sheng-Fuu
2011-12-01
Image alignment is considered a key problem in visual inspection applications. The main concerns for such tasks are fast image alignment with subpixel accuracy. About this, neural network-based approaches are very popular in visual inspection because of their high accuracy and efficiency of aligning images. However, such methods are difficult to identify the structure and parameters of neural network. In this study, a Takagi-Sugeno-Kang-type neuro-fuzzy network (NFN) with data-mining-based evolutionary learning algorithm (DMELA) is proposed. Compared with traditional learning algorithms, DMELA combines the self-organization algorithm (SOA), data-mining selection method (DMSM), and regularized least square (RLS) method to not only determine a suitable number of fuzzy rules, but also automatically tune the parameters of NFN. Experimental results are shown to demonstrate superior performance of the DMELA constructed image alignment system over other typical learning algorithms and existing alignment systems. Such system is useful to develop accurate and efficient image alignment systems.
NASA Astrophysics Data System (ADS)
Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.
2011-08-01
Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.
Nonlinear system identification of smart structures under high impact loads
NASA Astrophysics Data System (ADS)
Sarp Arsava, Kemal; Kim, Yeesock; El-Korchi, Tahar; Park, Hyo Seon
2013-05-01
The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure-MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.
Inference Concerning Physical Systems
NASA Astrophysics Data System (ADS)
Wolpert, David H.
The question of whether the universe "is" just an information- processing system has been extensively studied in physics. To address this issue, the canonical forms of information processing in physical systems - observation, prediction, control and memory - were analyzed in [24]. Those forms of information processing are all inherently epistemological; they transfer information concerning the universe as a whole into a scientist's mind. Accordingly, [24] formalized the logical relationship that must hold between the state of a scientist's mind and the state of the universe containing the scientist whenever one of those processes is successful. This formalization has close analogs in the analysis of Turing machines. In particular, it can be used to define an "informational analog" of algorithmic information complexity. In addition, this formalization allows us to establish existence and impossibility results concerning observation, prediction, control and memory. The impossibility results establish that Laplace was wrong to claim that even in a classical, non-chaotic universe the future can be unerringly predicted, given sufficient knowledge of the present. Alternatively, the impossibility results can be viewed as a non-quantum mechanical "uncertainty principle". Here I present a novel motivation of the formalization introduced in [24] and extend some of the associated impossibility results.
An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants
Lee, Ho-Hyun; Jang, Sang-Bok; Shin, Gang-Wook; Hong, Sung-Taek; Lee, Dae-Jong; Chun, Myung Geun
2015-01-01
Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant. PMID:26512666
Lee, Ho-Hyun; Jang, Sang-Bok; Shin, Gang-Wook; Hong, Sung-Taek; Lee, Dae-Jong; Chun, Myung Geun
2015-01-01
Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant. PMID:26512666
Estimation of dew point temperature using neuro-fuzzy and neural network techniques
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal
2013-11-01
This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.
System Support for Forensic Inference
NASA Astrophysics Data System (ADS)
Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan
Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach.
Chen, Chin-Fu; Feng, Xin; Szeto, Jack
2006-10-01
Gene expression profiling by microarray technology is usually difficult to interpret into a simpler pattern. One approach to resolve the complexity of gene expression profiles is the application of artificial neural networks (ANNs). A potential difficulty in this strategy, however, is that the non-linear nature of ANN makes it essentially a 'black-box' computation process. Addition of a fuzzy logic approach is useful because it can complement ANN by explicitly specifying membership function during computation. We employed a hybrid approach of neural network and fuzzy logic to further analyze a published microarray study of gene responses to eight bacteria in human macrophages. The original analysis by hierarchical clustering found common gene responses to all bacteria but did not address individual responses. Our method allowed exploration of the gene response of the host to individual bacterium. We implemented a two-layer, feed-forward neural network containing the principle of 'competitive learning' (i.e. 'winner-take-all'). The weights of the trained neural network were fed into a fuzzy logic inference system. A new measurement, called the impact rating (IR) was also introduced to explore the degree of importance of each gene. To assess the reliability of the IR value, a bootstrap re-sampling method was applied to the dataset and a confidence level for each IR was obtained. Our approach has successfully uncovered the unique features of host response to individual bacterium. Further, application of gene ontology (GO) annotation to the genes of high IR values in each response has suggested new biological pathways for individual host-pathogen interactions. PMID:16987708
Clustering of noisy image data using an adaptive neuro-fuzzy system
NASA Technical Reports Server (NTRS)
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
Inference for nonlinear dynamical systems
Ionides, E. L.; Bretó, C.; King, A. A.
2006-01-01
Nonlinear stochastic dynamical systems are widely used to model systems across the sciences and engineering. Such models are natural to formulate and can be analyzed mathematically and numerically. However, difficulties associated with inference from time-series data about unknown parameters in these models have been a constraint on their application. We present a new method that makes maximum likelihood estimation feasible for partially-observed nonlinear stochastic dynamical systems (also known as state-space models) where this was not previously the case. The method is based on a sequence of filtering operations which are shown to converge to a maximum likelihood parameter estimate. We make use of recent advances in nonlinear filtering in the implementation of the algorithm. We apply the method to the study of cholera in Bangladesh. We construct confidence intervals, perform residual analysis, and apply other diagnostics. Our analysis, based upon a model capturing the intrinsic nonlinear dynamics of the system, reveals some effects overlooked by previous studies. PMID:17121996
Asadi, Ali-Reza; Erfanian, Abbas
2012-07-01
During the last decade, intraspinal microstimulation (ISMS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoration of a desired functional limb movement through the use of ISMS is the development of a robust control strategy for determining the stimulation patterns. Accurate and stable control of limbs by functional intraspinal microstimulation is a very difficult task because neuromusculoskeletal systems have significant nonlinearity, time variability, large latency and time constant, and muscle fatigue. Furthermore, the controller must be able to compensate the effect of the dynamic interaction between motor neuron pools and electrode sites during ISMS. In this paper, we present a robust strategy for multi-joint control through ISMS in which the system parameters are adapted online and the controller requires no offline training phase. The method is based on the combination of sliding mode control with fuzzy logic and neural control. Extensive experiments on six rats are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed method. Despite the complexity of the spinal neuronal networks, our results show that the proposed strategy could provide accurate tracking control with fast convergence and could generate control signals to compensate for the effects of muscle fatigue. PMID:22711783
Prediction of low back pain with two expert systems.
Sari, Murat; Gulbandilar, Eyyup; Cimbiz, Ali
2012-06-01
Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation. PMID:20978929
Flood Forecasting in River System Using ANFIS
NASA Astrophysics Data System (ADS)
Ullah, Nazrin; Choudhury, P.
2010-10-01
The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.
Flood Forecasting in River System Using ANFIS
Ullah, Nazrin; Choudhury, P.
2010-10-26
The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.
Exploiting expert systems in cardiology: a comparative study.
Economou, George-Peter K; Sourla, Efrosini; Stamatopoulou, Konstantina-Maria; Syrimpeis, Vasileios; Sioutas, Spyros; Tsakalidis, Athanasios; Tzimas, Giannis
2015-01-01
An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients. PMID:25417018
Prediction of Heart Attack Risk Using GA-ANFIS Expert System Prototype.
Begic Fazlic, Lejla; Avdagic, Aja; Besic, Ingmar
2015-01-01
The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been used as inputs to classifiers being based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. They are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validating of the novel GA-ANFIS system approach is performed in MATLAB environment. We compared GA-ANFIS and ANFIS results. The proposed GA-ANFIS model with the predicted value technique is more efficient when diagnosis of heart disease is concerned, as well the earlier method we got by ANFIS model. PMID:25980885
GA-ANFIS Expert System Prototype for Prediction of Dermatological Diseases.
Begic Fazlic, Lejla; Avdagic, Korana; Omanovic, Samir
2015-01-01
This paper presents novel GA-ANFIS expert system prototype for dermatological disease detection by using dermatological features and diagnoses collected in real conditions. Nine dermatological features are used as inputs to classifiers that are based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. After that, they are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validation of the novel GA-ANFIS system approach is performed in MATLAB environment by using validation set of data. Some conclusions concerning the impacts of features on the detection of dermatological diseases were obtained through analysis of the GA-ANFIS. We compared GA-ANFIS and ANFIS results. The results confirmed that the proposed GA-ANFIS model achieved accuracy rates which are higher than the ones we got by ANFIS model. PMID:25991223
An inference engine for embedded diagnostic systems
NASA Technical Reports Server (NTRS)
Fox, Barry R.; Brewster, Larry T.
1987-01-01
The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.
NASA Astrophysics Data System (ADS)
Kumar, M. Ajay; Srikanth, N. V.
2014-11-01
The voltage source converter (VSC) based multiterminal high voltage direct current (MTDC) transmission system is an interesting technical option to integrate offshore wind farms with the onshore grid due to its unique performance characteristics and reduced power loss via extruded DC cables. In order to enhance the reliability and stability of the MTDC system, an adaptive neuro fuzzy inference system (ANFIS) based coordinated control design has been addressed in this paper. A four terminal VSC-MTDC system which consists of an offshore wind farm and oil platform is implemented in MATLAB/ SimPowerSystems software. The proposed model is tested under different fault scenarios along with the converter outage and simulation results show that the novel coordinated control design has great dynamic stabilities and also the VSC-MTDC system can supply AC voltage of good quality to offshore loads during the disturbances.
NASA Astrophysics Data System (ADS)
Saadeddin, Kamal; Abdel-Hafez, Mamoun F.; Jaradat, Mohammad A.; Jarrah, Mohammad Amin
2013-12-01
In this paper, a low-cost navigation system that fuses the measurements of the inertial navigation system (INS) and the global positioning system (GPS) receiver is developed. First, the system's dynamics are obtained based on a vehicle's kinematic model. Second, the INS and GPS measurements are fused using an extended Kalman filter (EKF) approach. Subsequently, an artificial intelligence based approach for the fusion of INS/GPS measurements is developed based on an Input-Delayed Adaptive Neuro-Fuzzy Inference System (IDANFIS). Experimental tests are conducted to demonstrate the performance of the two sensor fusion approaches. It is found that the use of the proposed IDANFIS approach achieves a reduction in the integration development time and an improvement in the estimation accuracy of the vehicle's position and velocity compared to the EKF based approach.
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
NASA Astrophysics Data System (ADS)
Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.
2010-09-01
Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
NASA Astrophysics Data System (ADS)
Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.
2011-01-01
Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
NASA Astrophysics Data System (ADS)
Akhoondzadeh, M.
2013-09-01
Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.
Single board system for fuzzy inference
NASA Technical Reports Server (NTRS)
Symon, James R.; Watanabe, Hiroyuki
1991-01-01
The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.
NASA Astrophysics Data System (ADS)
Vahidnia, Mohammad H.; Alesheikh, Ali A.; Alimohammadi, Abbas; Hosseinali, Farhad
2010-09-01
A significant portion of the Mazandaran Province in Iran is prone to landslides due to climatic conditions, excessive rain, geology, and geomorphologic characteristics. These landslides cause damage to property and pose a threat to human lives. Numerous solutions have been proposed to assess landslide susceptibility over regions such as this one. This study proposes an indirect assessment strategy that shares in the advantages of quantitative and qualitative assessment methods. It employs a fuzzy inference system (FIS) to model expert knowledge, and an artificial neural network (ANN) to identify non-linear behavior and generalize historical data to the entire region. The results of the FIS are averaged with the intensity values of existing landslides, and then used as outputs to train the ANN. The input patterns include both physical landscape characteristics (criterion maps) and landslide inventory maps. The ANN is trained with a modified back-propagation algorithm. As part of this study, the strategy is implemented as a GIS extension using ArcGIS®. This tool was used to create a four-domain landslide susceptibility map of the Mazandaran province. The overall accuracy of the LSM is estimated at 90.5%.
Inference System Integration Via Logic Morphisms
NASA Technical Reports Server (NTRS)
Bjorner, Nikolaj S.; Espinosa, David
2000-01-01
This is a final report on the accomplishments during the period of the NASA grant. The work on inference servers accomplished the integration of the SLANG logic (Specware's default specification logic) with a number of inference servers in order to make their complementary strengths available. These inverence servers are (1) SNARK. (2) Gandalf, Setheo, and Spass, (3) the Prototype Verification System (PVS) from SRI. (4) HOL98. We designed and implemented MetaSlang, an ML-like language, which we are using to specify and implement all our logic morphisms.
An Ada inference engine for expert systems
NASA Technical Reports Server (NTRS)
Lavallee, David B.
1986-01-01
The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.
NASA Astrophysics Data System (ADS)
Ali, Ali H.; Tarter, Alex
2009-05-01
The terrorist attack of 9/11 has revealed how vulnerable the civil aviation industry is from both security and safety points of view. Dealing with several aircrafts cruising in the sky of a specific region requires decision makers to have an automated system that can raise their situational awareness of how much a threat an aircraft presents. In this research, an in-flight array of sensors has been deployed in a simulated aircraft to extract knowledge-base information of how passengers and equipment behave in normal flighttime which has been used to train artificial neural networks to provide real-time streams of normal behaviours. Finally, a cascading of fuzzy logic networks is designed to measure the deviation of real-time data from the predicted ones. The results suggest that Neural-Fuzzy networks have a promising future to raise the awareness of decision makers about certain aviation situations.
Meta-learning framework applied in bioinformatics inference system design.
Arredondo, Toms; Ormazbal, Wladimir
2015-01-01
This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed. PMID:26255380
Inference by replication in densely connected systems
Neirotti, Juan P.; Saad, David
2007-10-15
An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance.
Inference by replication in densely connected systems.
Neirotti, Juan P; Saad, David
2007-10-01
An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance. PMID:17995074
Causal Inferences in the Campbellian Validity System
ERIC Educational Resources Information Center
Lund, Thorleif
2010-01-01
The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between
LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS
Lipton, D.L.; Wong, H.K.T.
1984-02-01
An inference is the process of transforming unclassified data values into confidential data values. Most previous research in inference control has studied the use of statistical aggregates to deduce individual records. However, several other types of inference are also possible. Unknown functional dependencies may be apparent to users who have 'expert' knowledge about the characteristics of a population. Some correlations between attributes may be concluded from 'commonly-known' facts about the world. To counter these threats, security managers should use random sampling of databases of similar populations, as well as expert systems. 'Expert' users of the DATABASE SYSTEM may form inferences from the variable performance of the user interface. Users may observe on-line turn-around time, accounting statistics. the error message received, and the point at which an interactive protocol sequence fails. One may obtain information about the frequency distributions of attribute values, and the validity of data object names from this information. At the back-end of a database system, improved software engineering practices will reduce opportunities to bypass functional units of the database system. The term 'DATA OBJECT' should be expanded to incorporate these data object types which generate new classes of threats. The security of DATABASES and DATABASE SySTEMS must be recognized as separate but related problems. Thus, by increased awareness of lower level inferences, system security managers may effectively nullify the threat posed by lower level inferences.
Grey box modelling and advanced control scheme for building heating systems
NASA Astrophysics Data System (ADS)
Jassar, Surinder
This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modeling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design an inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on the measurement of air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.
Diagnosis of arthritis through fuzzy inference system.
Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet
2012-06-01
Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process. PMID:20927572
Distributed intrusion detection system based on fuzzy rules
NASA Astrophysics Data System (ADS)
Qiao, Peili; Su, Jie; Liu, Yahui
2006-04-01
Computational Intelligence is the theory and method solving problems by simulating the intelligence of human using computer and it is the development of Artificial Intelligence. Fuzzy Technique is one of the most important theories of computational Intelligence. Genetic Fuzzy Technique and Neuro-Fuzzy Technique are the combination of Fuzzy Technique and novel techniques. This paper gives a distributed intrusion detection system based on fuzzy rules that has the characters of distributed parallel processing, self-organization, self-learning and self-adaptation by the using of Neuro-Fuzzy Technique and Genetic Fuzzy Technique. Specially, fuzzy decision technique can be used to reduce false detection. The results of the simulation experiment show that this intrusion detection system model has the characteristics of distributed, error tolerance, dynamic learning, and adaptation. It solves the problem of low identifying rate to new attacks and hidden attacks. The false detection rate is low. This approach is efficient to the distributed intrusion detection.
A knowledge-based expert system for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Kimes, Daniel S.; Harrison, Patrick R.; Ratcliffe, P. A.
1991-01-01
A prototype knowledge-based expert system VEG is presented that focuses on extracting spectral hemispherical reflectance using any combination of nadir and/or directional reflectance data as input. The system is designed to facilitate expansion to handle other inferences regarding vegetation properties such as total hemispherical reflectance, leaf area index, percent ground cover, phosynthetic capacity, and biomass. This approach is more robust and accurate than conventional extraction techniques previously developed.
An expert system shell for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1992-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown cover type data has been constructed. An interface that allows the results of VEG to be written to a file has been implemented. A learning system that learns class descriptions from a data base of historical cover type data and then uses the learned class descriptions to classify an unknown sample has been built. This system has an interface that integrates it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented.
Inference and learning in sparse systems with multiple states
NASA Astrophysics Data System (ADS)
Braunstein, A.; Ramezanpour, A.; Zecchina, R.; Zhang, P.
2011-05-01
We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.
Inference and learning in sparse systems with multiple states
Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.
2011-05-15
We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.
Evaluation of fuzzy inference systems using fuzzy least squares
NASA Technical Reports Server (NTRS)
Barone, Joseph M.
1992-01-01
Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.
Topological augmentation to infer hidden processes in biological systems
Sunnåker, Mikael; Zamora-Sillero, Elias; López García de Lomana, Adrián; Rudroff, Florian; Sauer, Uwe; Stelling, Joerg; Wagner, Andreas
2014-01-01
Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24297519
NASA Astrophysics Data System (ADS)
Pasam, Gopi Krishna; Manohar, T. Gowri
2015-07-01
Determination of available transfer capability (ATC) requires the use of experience, intuition and exact judgment in order to meet several significant aspects in the deregulated environment. Based on these points, this paper proposes two heuristic approaches to compute ATC. The first proposed heuristic algorithm integrates the five methods known as continuation repeated power flow, repeated optimal power flow, radial basis function neural network, back propagation neural network and adaptive neuro fuzzy inference system to obtain ATC. The second proposed heuristic model is used to obtain multiple ATC values. Out of these, a specific ATC value will be selected based on a number of social, economic, deregulated environmental constraints and related to specific applications like optimization, on-line monitoring, and ATC forecasting known as multi-objective decision based optimal ATC. The validity of results obtained through these proposed methods are scrupulously verified on various buses of the IEEE 24-bus reliable test system. The results presented and derived conclusions in this paper are very useful for planning, operation, maintaining of reliable power in any power system and its monitoring in an on-line environment of deregulated power system. In this way, the proposed heuristic methods would contribute the best possible approach to assess multiple objective ATC using integrated methods.
Improving real time flood forecasting using fuzzy inference system
NASA Astrophysics Data System (ADS)
Lohani, Anil Kumar; Goel, N. K.; Bhatia, K. K. S.
2014-02-01
In order to improve the real time forecasting of foods, this paper proposes a modified Takagi Sugeno (T-S) fuzzy inference system termed as threshold subtractive clustering based Takagi Sugeno (TSC-T-S) fuzzy inference system by introducing the concept of rare and frequent hydrological situations in fuzzy modeling system. The proposed modified fuzzy inference systems provide an option of analyzing and computing cluster centers and membership functions for two different hydrological situations, i.e. low to medium flows (frequent events) as well as high to very high flows (rare events) generally encountered in real time flood forecasting. The methodology has been applied for flood forecasting using the hourly rainfall and river flow data of upper Narmada basin, Central India. The available rainfall-runoff data has been classified in frequent and rare events and suitable TSC-T-S fuzzy model structures have been suggested for better forecasting of river flows. The performance of the model during calibration and validation is evaluated by performance indices such as root mean square error (RMSE), model efficiency and coefficient of correlation (R). In flood forecasting, it is very important to know the performance of flow forecasting model in predicting higher magnitude flows. The above described performance criteria do not express the prediction ability of the model precisely from higher to low flow region. Therefore, a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model. The developed model has been tested for different lead periods using hourly rainfall and discharge data. Further, the proposed fuzzy model results have been compared with artificial neural networks (ANN), ANN models for different classes identified by Self Organizing Map (SOM) and subtractive clustering based Takagi Sugeno fuzzy model (SC-T-S fuzzy model). It has been concluded from the study that the TSC-T-S fuzzy model provide reasonably accurate forecast with sufficient lead-time.
An expert system shell for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1993-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this contract, an interface to a file of unknown cover type data was constructed. An interface that allowed the results of VEG to be written to a file was also implemented. A learning system that learned class descriptions from a data base of historical cover type data and then used the learned class descriptions to classify an unknown sample was built. This system had an interface that integrated it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER was completed and a number of additional techniques that inferred the proportion ground cover of a sample were implemented. This work was previously described. The work carried out in the second year of the contract is described. The historical cover type database was removed from VEG and stored as a series of flat files that are external to VEG. An interface to the files was provided. The framework and interface for two new VEG subgoals that estimate the atmospheric effect on reflectance data were built. A new interface that allows the scientist to add techniques to VEG without assistance from the developer was designed and implemented. A prototype Help System that allows the user to get more information about each screen in the VEG interface was also added to VEG.
NASA Astrophysics Data System (ADS)
Volosencu, Constantin; Curiac, Daniel-Ioan
2013-12-01
This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods - linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system - to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.
ANUBIS: artificial neuromodulation using a Bayesian inference system.
Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie
2013-01-01
Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bzier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework. PMID:22970879
Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.
2014-01-01
Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829
Metainference: A Bayesian inference method for heterogeneous systems.
Bonomi, Massimiliano; Camilloni, Carlo; Cavalli, Andrea; Vendruscolo, Michele
2016-01-01
Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called "metainference," that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors. PMID:26844300
Metainference: A Bayesian inference method for heterogeneous systems
Bonomi, Massimiliano; Camilloni, Carlo; Cavalli, Andrea; Vendruscolo, Michele
2016-01-01
Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors. PMID:26844300
Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
2009-04-01
Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.
A primer on Bayesian inference for biophysical systems.
Hines, Keegan E
2015-05-01
Bayesian inference is a powerful statistical paradigm that has gained popularity in many fields of science, but adoption has been somewhat slower in biophysics. Here, I provide an accessible tutorial on the use of Bayesian methods by focusing on example applications that will be familiar to biophysicists. I first discuss the goals of Bayesian inference and show simple examples of posterior inference using conjugate priors. I then describe Markov chain Monte Carlo sampling and, in particular, discuss Gibbs sampling and Metropolis random walk algorithms with reference to detailed examples. These Bayesian methods (with the aid of Markov chain Monte Carlo sampling) provide a generalizable way of rigorously addressing parameter inference and identifiability for arbitrarily complicated models. PMID:25954869
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris
2013-01-01
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245
Perturbation biology: inferring signaling networks in cellular systems.
Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B; Pratilas, Christine A; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris
2013-01-01
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245
A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments
NASA Technical Reports Server (NTRS)
Hancock, Thomas M., III
1994-01-01
This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.
Erratum to Central European Journal of Engineering, Volume 4, Issue 1
NASA Astrophysics Data System (ADS)
Kumar, M.; Srikanth, N.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
Erratum: Erratum to Central European Journal of Engineering, Volume 4, Issue 1
NASA Astrophysics Data System (ADS)
Kumar, M. Ajay; Srikanth, N. V.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)
NASA Technical Reports Server (NTRS)
Truong, Son H.
1999-01-01
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.
An expert system shell for inferring vegetation characteristics: Prototype help system (Task 1)
NASA Technical Reports Server (NTRS)
1993-01-01
The NASA Vegetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. A prototype of the VEG subgoal HELP.SYSTEM has been completed and the Help System has been added to the VEG system. It is loaded when the user first clicks on the HELP.SYSTEM option in the Tool Box Menu. The Help System provides a user tool to support needed user information. It also provides interactive tools the scientist may use to develop new help messages and to modify existing help messages that are attached to VEG screens. The system automatically manages system and file operations needed to preserve new or modified help messages. The Help System was tested both as a help system development and a help system user tool.
A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)
NASA Technical Reports Server (NTRS)
Truong, S. H.
1999-01-01
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.
Inferring the Gibbs state of a small quantum system
Rau, Jochen
2011-07-15
Gibbs states are familiar from statistical mechanics, yet their use is not limited to that domain. For instance, they also feature in the maximum entropy reconstruction of quantum states from incomplete measurement data. Outside the macroscopic realm, however, estimating a Gibbs state is a nontrivial inference task, due to two complicating factors: the proper set of relevant observables might not be evident a priori; and whenever data are gathered from a small sample only, the best estimate for the Lagrange parameters is invariably affected by the experimenter's prior bias. I show how the two issues can be tackled with the help of Bayesian model selection and Bayesian interpolation, respectively, and illustrate the use of these Bayesian techniques with a number of simple examples.
Abraham, Ivo L.; Schultz, Samuel; Ozbolt, Judy G.; Swain, Mary Ann P.
1984-01-01
This article presents the procedures for clinical inference of a multivariate algorithm for diagnostic information systems. It complements an earlier paper by the authors which described the procedures for data-acquisition and storage of the proposed algorithm. Clinical inference is viewed as a twofold process of (1) acquiring and sampling information, and organizing it in a fashion conducive to subsequent analysis; and (2) deciding about the (ab)normality of functional health domains. Normality is defined as being within the limits of a population criterion interval. The mathematical explication of clinical inference as used in the algorithm constitutes the body of this article.
Lin, Chin-Teng; Tsai, Shu-Fang; Ko, Li-Wei
2013-10-01
Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of ~82%. PMID:24808604
Comparison of soft computing systems for the post-calibration of weather radar
NASA Astrophysics Data System (ADS)
Hessami Kermani, Masoud Reza
The most usual tools to monitor rainfall events are raingauges and weather radar. Networks of raingauges provide accurate point estimates of rainfall, when appropriately set, but their usual low density restricts considerably the spatial resolution of the gathered information. Such networks, with rain gauges at distinct points, do not reflect the spatial distribution of rainfall. The quality of raingauge observations is also susceptible to some error sources, for example wind effects around the raingauges and poor raingauge reports due to hardware problems. Radar systems offer high spatial and temporal resolution observation which is much more efficient at providing the space-time evolution of a rainfall event in comparison with raingauge networks. However the radar measurements are not free of errors due to a variety of factors including ground clutter, bright bands, anomalous propagation, beam blockages, and attenuation. The effectiveness of weather radar operation is strongly linked to rigorous calibration. Various methods have been proposed to calibrate radar data. They can be classified into two main categories: deterministic and statistical. The deterministic approach involves the calibration of radar rainfall estimations against raingauge observations. The statistical approach includes multivariate analysis and cokriging. Geostatistical approaches are known as the best methods for radar-raingauge data integration but they are usually inefficient in real time, especially when dealing with the sampling rates of one hour or less necessary for urban and small watershed applications. Such methods also rely on a strong human expertise which can lead to user-dependent results. The objectives of this research are to introduce and to investigate the feasibility of soft computing systems for the post-calibration of weather radar in comparison with the best existing method based on geostatistics. In this work, the soft computing systems include artificial neural networks and Adaptive Neuro-Fuzzy Inference System (ANFIS) and the geostatistical approach includes residual kriging. The residual kriging calibration results are satisfying however this method is based on stationary hypotheses and requires variogram modeling, making it difficult in an operational context. This method has the advantage of providing a mean squared errors map based on variogram modeling for the estimations. For the artificial neural network, thirteen variants of the multilayer feedforward networks and two variants of radial basis functions are tested in this work. The neural calibration results showed that the Levenberg-Marquardt algorithm using Bayesian regularization is robust and reliable for radar-raingauge data integration. The ANFIS offers the precision and learning capability of artificial neural networks combined with the advantages of fuzzy logic. This method based on the Jackknife approach allows the use of all the available data for training and checking the neuro-fuzzy inference system, and provides a degree of reliability of the post-calibration. The training and the interpolation results of proposed methods can be obtained within just a few seconds using an ordinary personal computer, which is incomparably faster than geostatistical approaches. The proposed algorithms would be very efficient for real time post-calibration.
NASA Astrophysics Data System (ADS)
Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer
2015-03-01
Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.
Parameter and Structure Inference for Nonlinear Dynamical Systems
NASA Technical Reports Server (NTRS)
Morris, Robin D.; Smelyanskiy, Vadim N.; Millonas, Mark
2006-01-01
A great many systems can be modeled in the non-linear dynamical systems framework, as x = f(x) + xi(t), where f() is the potential function for the system, and xi is the excitation noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications.
a Study of K-P Interaction at High Energy Using Adaptive Fuzzy Inference System Interactions
NASA Astrophysics Data System (ADS)
El-Bakry, M. Y.
Adaptive Network Fuzzy Inference System (ANFIS) is an artificial intelligence (AI)-based technique that proved efficient in a variety of problems such as classification, recognition and modeling of complex systems. This paper utilizes the adaptive network fuzzy inference system to model the K-P interactions. The ANFIS-based K-P model simulates the multiplicity distribution of charged pions at different high energies. The results showed very accurate fitting to the experimental data recommending it to be a good alternative to other theoretical techniques.
FINDS: A fault inferring nonlinear detection system. User's guide
NASA Technical Reports Server (NTRS)
Lancraft, R. E.; Caglayan, A. K.
1983-01-01
The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.
Earth system sensitivity inferred from Pliocene modelling and data
Lunt, D.J.; Haywood, A.M.; Schmidt, G.A.; Salzmann, U.; Valdes, P.J.; Dowsett, H.J.
2010-01-01
Quantifying the equilibrium response of global temperatures to an increase in atmospheric carbon dioxide concentrations is one of the cornerstones of climate research. Components of the Earths climate system that vary over long timescales, such as ice sheets and vegetation, could have an important effect on this temperature sensitivity, but have often been neglected. Here we use a coupled atmosphere-ocean general circulation model to simulate the climate of the mid-Pliocene warm period (about three million years ago), and analyse the forcings and feedbacks that contributed to the relatively warm temperatures. Furthermore, we compare our simulation with proxy records of mid-Pliocene sea surface temperature. Taking these lines of evidence together, we estimate that the response of the Earth system to elevated atmospheric carbon dioxide concentrations is 30-50% greater than the response based on those fast-adjusting components of the climate system that are used traditionally to estimate climate sensitivity. We conclude that targets for the long-term stabilization of atmospheric greenhouse-gas concentrations aimed at preventing a dangerous human interference with the climate system should take into account this higher sensitivity of the Earth system. ?? 2010 Macmillan Publishers Limited. All rights reserved.
An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1992-01-01
This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.
Evaluating Delivery Systems: Complex Evaluations and Plausibility Inference
Webster, Jayne; Kweku, Margaret; Dedzo, McDamien; Tinkorang, Kojo; Bruce, Jane; Lines, Jo; Chandramohan, Daniel; Hanson, Kara
2010-01-01
Delivery system evaluation is poorly defined and therefore a barrier to achieving increased coverage of interventions. We use a pre- and post-implementation cross-sectional observational study with assessment of the intermediate processes to evaluate a new delivery system for insecticide-treated nets (ITNs) in two regions of Ghana. In Volta Region, ownership of at least one net rose from 38.3% to 45.4% (P = 0.06), and 6.5% of respondents used a voucher in the purchase. In Eastern Region, ownership of a net rose from 13.7% to 26.0% (P < 0.001) and 0.5% of households used a voucher to purchase a net. Just 40.7% and 21.1% of eligible antenatal clinic (ANC) attendees were offered a voucher in Volta and Eastern Regions, respectively, and 36.0% and 30.7% used their voucher in the purchase of an ITN. Without attributing nets to the specific delivery system, in Eastern Region the success of the new system would be overestimated. PMID:20348517
Daniels, Bryan C.; Nemenman, Ilya
2015-01-01
The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem. PMID:25806510
An Improved Storage and Inference Method for Ontology Based Remote Sensing Interpretation System
NASA Astrophysics Data System (ADS)
Jia, Xiaoguang; Lin, Zhengwei; Huang, Ning
As the incredibly expanding volumes of remote sensing archives, and the number of objects necessary to identify in remote sensing pictures increasing, the conventional process of remote sensing interpretation is becoming more and more inefficient, and it seems impossible to finish all interpretation tasks in time. This paper applies ontology techniques to this process. It uses ontology to describe the domain knowledge, and brings forward a new hybrid ontology mapping model and an effective inference method without using any ontology inference engine. Finally it constructs an interpretation system, using which the work efficiency can be improved greatly.
Large-Scale Optimization for Bayesian Inference in Complex Systems
Willcox, Karen; Marzouk, Youssef
2013-11-12
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.
NASA Astrophysics Data System (ADS)
Vrettas, Michail D.; Opper, Manfred; Cornford, Dan
2015-01-01
This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.
NASA Technical Reports Server (NTRS)
Harrison, P. Ann
1992-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'
NASA Technical Reports Server (NTRS)
Ding, Y. J.; Hong, Q. F.; Hagyard, M. J.; Deloach, A. C.; Liu, X. P.
1987-01-01
Techniques to identify sources of electric current systems and their channels of flow in solar active regions are explored. Measured photospheric vector magnetic fields together with high-resolution white-light and H-alpha filtergrams provide the data base to derive the current systems in the photosphere and chromosphere. As an example, the techniques are then applied to infer current systems in AR 2372 in early April 1980.
The state of the atmosphere as inferred from the FGGE satellite observing systems during SOP-1
NASA Technical Reports Server (NTRS)
Halem, M.; Kalnay, E.; Baker, W. E.; Atlas, R.
1981-01-01
Data assimilation experiments were performed to test the influence of different elements of the satellite observing systems. Results from some of the experiments are presented. These findings show that the FGGE satellite systems are able to infer the three-dimensional motion field and improve the representation of the large-scale state of the atmosphere. Preliminary results of the forecast impact of the FGGE data sets are also presented.
Olyaie, Ehsan; Banejad, Hossein; Chau, Kwok-Wing; Melesse, Assefa M
2015-04-01
Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values. PMID:25787167
Petrophysical data prediction from seismic attributes using committee fuzzy inference system
NASA Astrophysics Data System (ADS)
Kadkhodaie-Ilkhchi, Ali; Rezaee, M. Reza; Rahimpour-Bonab, Hossain; Chehrazi, Ali
2009-12-01
This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation ( S w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.
Magliano, Joseph P; Larson, Adam M; Higgs, Karyn; Loschky, Lester C
2016-02-01
This study investigated the relative roles of visuospatial versus linguistic working memory (WM) systems in the online generation of bridging inferences while viewers comprehend visual narratives. We contrasted these relative roles in the visuospatial primacy hypothesis versus the shared (visuospatial & linguistic) systems hypothesis, and tested them in 3 experiments. Participants viewed picture stories containing multiple target episodes consisting of a beginning state, a bridging event, and an end state, respectively, and the presence of the bridging event was manipulated. When absent, viewers had to infer the bridging-event action to comprehend the end-state image. A pilot study showed that after viewing the end-state image, participants' think-aloud protocols contained more inferred actions when the bridging event was absent than when it was present. Likewise, Experiment 1 found longer viewing times for the end-state image when the bridging-event image was absent, consistent with viewing times revealing online inference generation processes. Experiment 2 showed that both linguistic and visuospatial WM loads attenuated the inference viewing time effect, consistent with the shared systems hypothesis. Importantly, however, Experiment 3 found that articulatory suppression did not attenuate the inference viewing time effect, indicating that (sub)vocalization did not support online inference generation during visual narrative comprehension. Thus, the results support a shared-systems hypothesis in which both visuospatial and linguistic WM systems support inference generation in visual narratives, with the linguistic WM system operating at a deeper level than (sub)vocalization. PMID:26450589
Aggelopoulos, Nikolaos C
2015-08-01
Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. PMID:25976632
FINDS: A fault inferring nonlinear detection system programmers manual, version 3.0
NASA Technical Reports Server (NTRS)
Lancraft, R. E.
1985-01-01
Detailed software documentation of the digital computer program FINDS (Fault Inferring Nonlinear Detection System) Version 3.0 is provided. FINDS is a highly modular and extensible computer program designed to monitor and detect sensor failures, while at the same time providing reliable state estimates. In this version of the program the FINDS methodology is used to detect, isolate, and compensate for failures in simulated avionics sensors used by the Advanced Transport Operating Systems (ATOPS) Transport System Research Vehicle (TSRV) in a Microwave Landing System (MLS) environment. It is intended that this report serve as a programmers guide to aid in the maintenance, modification, and revision of the FINDS software.
From free energy measurements to thermodynamic inference in nonequilibrium small systems
NASA Astrophysics Data System (ADS)
Alemany, A.; Ribezzi-Crivellari, M.; Ritort, F.
2015-07-01
Fluctuation theorems (FTs), such as the Crooks or Jarzynski equalities (JEs), have become an important tool in single-molecule biophysics where they allow experimentalists to exploit thermal fluctuations and measure free-energy differences from non-equilibrium pulling experiments. The rich phenomenology of biomolecular systems has stimulated the development of extensions to the standard FTs, to encompass different experimental situations. Here we discuss an extension of the Crooks fluctuation relation that allows the thermodynamic characterization of kinetic molecular states. This extension can be connected to the generalized JE under feedback. Finally we address the recently introduced concept of thermodynamic inference or how FTs can be used to extract the total entropy production distribution in nonequilibrium systems from partial entropy production measurements. We discuss the significance of the concept of effective temperature in this context and show how thermodynamic inference provides a unifying comprehensive picture in several nonequilibrium problems.
A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.
Kilic, Yusuf Alper; Kilic, Ilke
2010-12-01
Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors. PMID:20703599
Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I
2006-01-01
In this work, three different methodologies for fuzzy expert systems creation are compared: a well-known neuro-fuzzy approach, a knowledge-based approach and a novel methodology, based on rule-extraction. The adaptive neuro-fuzzy information system (ANFIS) is used to automatically generate a fuzzy expert system. In the knowledge-based approach and the rule-extraction methodology, the idea is to start with a model described by crisp rules, provided by medical experts in the first case or extracted using data mining techniques in the second, and then to transform them into a set of fuzzy rules, creating a fuzzy model. In either case, the adjustment of the model's parameters is performed via a stochastic global optimization procedure. All three approaches are applied to a medical domain problem, the cardiac arrhythmic beat classification. The ability to interpret the decisions made from the created fuzzy expert systems is a major advantage compared to other "black box" approaches. PMID:17946103
A new GRASS GIS fuzzy inference system for massive data analysis
NASA Astrophysics Data System (ADS)
Jasiewicz, Jaros?aw
2011-09-01
GIS systems are frequently coupled with fuzzy logic systems implemented in statistical packages. For large GIS data sets including millions or tens of millions of cells, such an approach is relatively time-consuming. For very large data sets there is also an input/output bottleneck between the GIS and external software. The aim of this paper is to present low-level implementation of Mamdani's fuzzy inference system designed to work with massive GIS data sets, using the GRASS GIS raster data processing engine.
Path-space variational inference for non-equilibrium coarse-grained systems
NASA Astrophysics Data System (ADS)
Harmandaris, Vagelis; Kalligiannaki, Evangelia; Katsoulakis, Markos; Plecháč, Petr
2016-06-01
In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.
GenePath: a system for inference of genetic networks and proposal of genetic experiments.
Zupan, Blaz; Bratko, Ivan; Demsar, Janez; Juvan, Peter; Curk, Tomaz; Borstnik, Urban; Beck, J Robert; Halter, John; Kuspa, Adam; Shaulsky, Gad
2003-01-01
A genetic network is a formalism that is often used in biology to represent causalities and reason about biological phenomena related to genetic regulation. We present GenePath, a computer-based system that supports the inference of genetic networks from a set of genetic experiments. Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on background knowledge and experimental results. Additionally, it can propose genetic experiments that may further refine the discovered network and establish relations between genes that could not be related based on the original experimental data. We illustrate GenePath's approach and utility on analysis of data on aggregation and sporulation of the soil amoeba Dictyostelium discoideum. PMID:12957783
Fuzzy systems in high-energy physics
NASA Astrophysics Data System (ADS)
Castellano, Marcello; Masulli, Francesco; Penna, Massimo
1996-06-01
Decision making is one of the major subjects of interest in physics. This is due to the intrinsic finite accuracy of measurement that leads to the possible results to span a region for each quantity. In this way, to recognize a particle type among the others by a measure of a feature vector, a decision must be made. The decision making process becomes a crucial point whenever a low statistical significance occurs as in space cosmic ray experiments where searching in rare events requires us to reject as many background events as possible (high purity), keeping as many signal events as possible (high efficiency). In the last few years, interesting theoretical results on some feedforward connectionist systems (FFCSs) have been obtained. In particular, it has been shown that multilayer perceptrons (MLPs), radial basis function networks (RBFs), and some fuzzy logic systems (FLSs) are nonlinear universal function approximators. This property permits us to build a system showing intelligent behavior , such as function estimation, time series forecasting, and pattern classification, and able to learn their skill from a set of numerical data. From the classification point of view, it has been demonstrated that non-parametric classifiers based FFCSs holding the universal function approximation property, can approximate the Bayes optimal discriminant function and then minimize the classification error. In this paper has been studied the FBF when applied to a high energy physics problem. The FBF is a powerful neuro-fuzzy system (or adaptive fuzzy logic system) holding the universal function approximation property and the capability of learning from examples. The FBF is based on product-inference rule (P), the Gaussian membership function (G), a singleton fuzzifier (S), and a center average defuzzifier (CA). The FBF can be regarded as a feedforward connectionist system with just one hidden layer whose units correspond to the fuzzy MIMO rules. The FBF can be identified both by exploiting the linguistic knowledge available (structure identification problem) and by using the information contained in a data set (parameter estimation problem). The fuzzy system has been found to be effective for the classification tasks of about 2 by 10-3 hadron contamination at 90% of electron acceptance. A comparison between the adaptive system results and the others previous ones obtained by using both statistical and neural network based methodologies also is presented.
Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft
NASA Astrophysics Data System (ADS)
Sadok, Mokhtar; Zakrzewski, Radek; Zeliff, Bob
2005-02-01
Conventional smoke detection systems currently installed onboard aircraft are often subject to high rates of false alarms. Under current procedures, whenever an alarm is issued the pilot is obliged to release fire extinguishers and to divert to the nearest airport. Aircraft diversions are costly and dangerous in some situations. A reliable detection system that minimizes false-alarm rate and allows continuous monitoring of cargo compartments is highly desirable. A video-based system has been recently developed by Goodrich Corporation to address this problem. The Cargo Fire Verification System (CFVS) is a multi camera system designed to provide live stream video to the cockpit crew and to perform hotspot, fire, and smoke detection in aircraft cargo bays. In addition to video frames, the CFVS uses other sensor readings to discriminate between genuine events such as fire or smoke and nuisance alarms such as fog or dust. A Mamdani-type fuzzy inference engine is developed to provide approximate reasoning for decision making. In one implementation, Gaussian membership functions for frame intensity-based features, relative humidity, and temperature are constructed using experimental data to form the system inference engine. The CFVS performed better than conventional aircraft smoke detectors in all standardized tests.
2013-01-01
Background Model selection and parameter inference are complex problems that have yet to be fully addressed in systems biology. In contrast with parameter optimisation, parameter inference computes both the parameter means and their standard deviations (or full posterior distributions), thus yielding important information on the extent to which the data and the model topology constrain the inferred parameter values. Results We report on the application of nested sampling, a statistical approach to computing the Bayesian evidence Z, to the inference of parameters, and the estimation of log Z in an established model of circadian rhythms. A ten-fold difference in the coefficient of variation between degradation and transcription parameters is demonstrated. We further show that the uncertainty remaining in the parameter values is reduced by the analysis of increasing numbers of circadian cycles of data, up to 4 cycles, but is unaffected by sampling the data more frequently. Novel algorithms for calculating the likelihood of a model, and a characterisation of the performance of the nested sampling algorithm are also reported. The methods we develop considerably improve the computational efficiency of the likelihood calculation, and of the exploratory step within nested sampling. Conclusions We have demonstrated in an exemplar circadian model that the estimates of posterior parameter densities (as summarised by parameter means and standard deviations) are influenced predominately by the length of the time series, becoming more narrowly constrained as the number of circadian cycles considered increases. We have also shown the utility of the coefficient of variation for discriminating between highly-constrained and less-well constrained parameters. PMID:23899119
Hanemaaijer, Mark; Röling, Wilfred F. M.; Olivier, Brett G.; Khandelwal, Ruchir A.; Teusink, Bas; Bruggeman, Frank J.
2015-01-01
Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist. PMID:25852671
Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo
This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.
Free-energy inference from partial work measurements in small systems
Ribezzi-Crivellari, Marco; Ritort, Felix
2014-01-01
Fluctuation relations (FRs) are among the few existing general results in nonequilibrium systems. Their verification requires the measurement of the total work performed on a system. Nevertheless in many cases only a partial measurement of the work is possible. Here we consider FRs in dual-trap optical tweezers where two different forces (one per trap) are measured. With this setup we perform pulling experiments on single molecules by moving one trap relative to the other. We demonstrate that work should be measured using the force exerted by the trap that is moved. The force that is measured in the trap at rest fails to provide the full dissipation in the system, leading to a (incorrect) work definition that does not satisfy the FR. The implications to single-molecule experiments and free-energy measurements are discussed. In the case of symmetric setups a second work definition, based on differential force measurements, is introduced. This definition is best suited to measure free energies as it shows faster convergence of estimators. We discuss measurements using the (incorrect) work definition as an example of partial work measurement. We show how to infer the full work distribution from the partial one via the FR. The inference process does also yield quantitative information, e.g., the hydrodynamic drag on the dumbbell. Results are also obtained for asymmetric dual-trap setups. We suggest that this kind of inference could represent a previously unidentified and general application of FRs to extract information about irreversible processes in small systems. PMID:25099353
2011-01-01
Background In recent years, phylogeographic studies have produced detailed knowledge on the worldwide distribution of mitochondrial DNA (mtDNA) variants, linking specific clades of the mtDNA phylogeny with certain geographic areas. However, a multiplex genotyping system for the detection of the mtDNA haplogroups of major continental distribution that would be desirable for efficient DNA-based bio-geographic ancestry testing in various applications is still missing. Results Three multiplex genotyping assays, based on single-base primer extension technology, were developed targeting a total of 36 coding-region mtDNA variants that together differentiate 43 matrilineal haplo-/paragroups. These include the major diagnostic haplogroups for Africa, Western Eurasia, Eastern Eurasia and Native America. The assays show high sensitivity with respect to the amount of template DNA: successful amplification could still be obtained when using as little as 4 pg of genomic DNA and the technology is suitable for medium-throughput analyses. Conclusions We introduce an efficient and sensitive multiplex genotyping system for bio-geographic ancestry inference from mtDNA that provides resolution on the continental level. The method can be applied in forensics, to aid tracing unknown suspects, as well as in population studies, genealogy and personal ancestry testing. For more complete inferences of overall bio-geographic ancestry from DNA, the mtDNA system provided here can be combined with multiplex systems for suitable autosomal and, in the case of males, Y-chromosomal ancestry-sensitive DNA markers. PMID:21429198
Use of fuzzy inference system for condition monitoring of induction motor
NASA Astrophysics Data System (ADS)
Janier, Josefina B.; Zaim Zaharia, M. F.; Karim, Samsul Ariffin Abd.
2012-09-01
Three phase induction motors are commonly used in industry due to its robustness, simplicity of its construction and high reliability. The tasks performed by these motors grow increasingly complex because of modern industries hence there is a need to determine the faults. Early detection of faults will reduce an unscheduled machine downtime that can upset production deadlines and may cause heavy financial losses. This paper is focused in developing a computer based system using Fuzzy Inference system's membership function. An unusual increase in vibration of the motor could be an indicator of faulty condition hence the vibration of the motor of an induction motor was used as an input, whereas the output is the motor condition. An inference system of the Fuzzy Logic was created to classify the vibration characteristics of the motor which is called vibration analysis. The system classified the motor of the gas distribution pump condition as from 'acceptable' to 'monitor closely'. The early detection of unusual increase in vibration of the induction motor is an important part of a predictive maintenance for motor driven machinery.
The early solar system abundance of /sup 244/Pu as inferred from the St. Severin chondrite
Hudson, G.B.; Kennedy, B.M.; Podosek, F.A.; Hohenberg, C.M.
1987-03-01
We describe the analysis of Xe released in stepwise heating of neutron-irradiated samples of the St. Severin chondrite. This analysis indicates that at the time of formation of most chondritic meteorites, approximately 4.56 x 10/sup 9/ years ago, the atomic ratio of /sup 244/Pu//sup 238/U was 0.0068 +- 0.0010 in chondritic meteorites. We believe that this value is more reliable than that inferred from earlier analyses of St. Severin. We feel that this value is currently the best available estimate for the early solar system abundance of /sup 244/Pu. 42 refs., 2 tabs.
NASA Astrophysics Data System (ADS)
Zhang, Daili
Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.
NASA Astrophysics Data System (ADS)
Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.
2015-11-01
This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.
A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference.
Chiang, Tzu-Chiang; Liang, Wen-Hua
2015-09-01
In the present society, most families are double-income families, and as the long-term care is seriously short of manpower, it contributes to the rapid development of tele-homecare equipment, and the smart home care system gradually emerges, which assists the elderly or patients with chronic diseases in daily life. This study aims at interaction between persons under care and the system in various living spaces, as based on motion-sensing interaction, and the context-aware smart home care system is proposed. The system stores the required contexts in knowledge ontology, including the physiological information and environmental information of the person under care, as the database of decision. The motion-sensing device enables the person under care to interact with the system through gestures. By the inference mechanism of fuzzy theory, the system can offer advice and rapidly execute service, thus, implementing the EHA. In addition, the system is integrated with the functions of smart phone, tablet PC, and PC, in order that users can implement remote operation and share information regarding the person under care. The health care system constructed in this study enables the decision making system to probe into the health risk of each person under care; then, from the view of preventive medicine, and through a composing system and simulation experimentation, tracks the physiological trend of the person under care, and provides early warning service, thus, promoting smart home care. PMID:26265236
Another expert system rule inference based on DNA molecule logic gates
NASA Astrophysics Data System (ADS)
WÄ siewicz, Piotr
2013-10-01
With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.
1986-01-01
This paper summarizes the modifications made to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of an update frequency, are also presented.
Inference of Disease-Related Molecular Logic from Systems-Based Microarray Analysis
Varadan, Vinay; Anastassiou, Dimitris
2006-01-01
Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage. PMID:16789819
An adaptive predictor for dynamic system forecasting
NASA Astrophysics Data System (ADS)
Wang, Wilson
2007-02-01
A reliable and real-time predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive predictor is developed based on the neuro-fuzzy approach to dynamic system forecasting. An adaptive training technique is proposed to improve forecasting performance, accommodate different operation conditions, and prevent possible trapping due to local minima. The viability of the developed predictor is evaluated by using both gear system condition monitoring and material fatigue testing. The investigation results show that the developed adaptive predictor is a reliable and robust forecasting tool. It can capture the system's dynamic behaviour quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.
Development of neural network techniques for finger-vein pattern classification
NASA Astrophysics Data System (ADS)
Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen
2010-02-01
A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.
Portable inference engine: An extended CLIPS for real-time production systems
NASA Technical Reports Server (NTRS)
Le, Thach; Homeier, Peter
1988-01-01
The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.
Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications
NASA Astrophysics Data System (ADS)
Chen, Cheng-Hung; Liao, Yen-Yun
2014-04-01
This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the functional-link-based neurofuzzy inference system (FLNIS) for prediction applications. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. The proposed TPSO uses a self-clustering algorithm to divide the particle swarm into multiple tribes, and selects suitable evolution strategies to update each particle. The TPSO also uses a tribal adaptation mechanism to remove and generate particles and reconstruct tribal links. The tribal adaptation mechanism can improve the qualities of the tribe and the tribe adaptation. Finally, the FLNIS model with the proposed TPSO (FLNIS-TPSO) was used in several predictive applications. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.
An expert system shell for inferring vegetation characteristics: Atmospheric techniques (Task G)
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1993-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoals has been implemented. The subgoal Atmospheric Passes allows the scientist to take reflectance data measured at ground level and predict what the reflectance values would be if the data were measured at a different atmospheric height. The subgoal Atmospheric Corrections allows atmospheric corrections to be made to data collected from an aircraft or by a satellite to determine what the equivalent reflectance values would be if the data were measured at ground level. The report describes the implementation and testing of the basic framework and interface for the Atmospheric Techniques Subgoals.
The early solar system abundance of Pu-244 as inferred from the St. Severin chondrite
NASA Technical Reports Server (NTRS)
Hudson, G. B.; Kennedy, B. M.; Podosek, F. A.; Hohenberg, C. M.
1989-01-01
The isotopic composition of Xe released in stepwise heating of neutron-irradiated samples of the St. Severin chondrite was measured. This analysis shows that at the time of formation of most chondritic meteorites, approximately 4.56 x 10 to the 9th yr ago, the atomic ratio of Pu-244/U-238 was 0.0068 + or - 0.0010 in chondritic meteorites. This value is believed to be more reliable that inferred from earlier analyses of St. Severin and is the best estimate for the early solar system abundance of Pu-244. The comparison of Xe compositions between irradiated and unirradiated samples shows that the composition of trapped (ambient) Xe in St. Severin has a significantly lower value of Xe-136/Xe-130 than average carbonaceous chondrites.
Fuzzy inference system for identification of geological stratigraphy off Prydz Bay, East Antarctica
NASA Astrophysics Data System (ADS)
Singh, Upendra K.
2011-12-01
The analysis of well logging data plays key role in the exploration and development of hydrocarbon reservoirs. Various well log parameters such as porosity, gamma ray, density, transit time and resistivity, help in classification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular geological stratigraphy formation are function of its composition, physical properties that help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify the kind of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the kinds of stratigraphy from well logs over Prydz bay basin, East Antarctica using fuzzy inference system. A model is built based on few data sets of known stratigraphy and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. Initially the fuzzy based algorithm is trained, validated and tested on well log data and finally identifies the formation lithology of a hydrocarbon reservoir system of study area. The effectiveness of this technique is demonstrated by the analysis of the results for actual lithologs and coring data of ODP Leg 188. The fuzzy results show that the training performance equals to 82.95% while the prediction ability is 87.69%. The fuzzy results are very encouraging and the model is able to decipher even thin layer seams and other strata from geophysical logs. The result provides the significant sand formation of depth range 316.0- 341.0 m, where core recovery is incomplete.
Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai
2016-01-01
Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639
Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai
2016-01-01
Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639
Schumacher, Johannes; Wunderle, Thomas; Fries, Pascal; Jäkel, Frank; Pipa, Gordon
2015-08-01
In neuroscience, data are typically generated from neural network activity. The resulting time series represent measurements from spatially distributed subsystems with complex interactions, weakly coupled to a high-dimensional global system. We present a statistical framework to estimate the direction of information flow and its delay in measurements from systems of this type. Informed by differential topology, gaussian process regression is employed to reconstruct measurements of putative driving systems from measurements of the driven systems. These reconstructions serve to estimate the delay of the interaction by means of an analytical criterion developed for this purpose. The model accounts for a range of possible sources of uncertainty, including temporally evolving intrinsic noise, while assuming complex nonlinear dependencies. Furthermore, we show that if information flow is delayed, this approach also allows for inference in strong coupling scenarios of systems exhibiting synchronization phenomena. The validity of the method is demonstrated with a variety of delay-coupled chaotic oscillators. In addition, we show that these results seamlessly transfer to local field potentials in cat visual cortex. PMID:26079751
A novel bridge scour monitoring and prediction system
NASA Astrophysics Data System (ADS)
Valyrakis, Manousos; Michalis, Panagiotis; Zhang, Hanqing
2015-04-01
Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the use of a novel methodology is proposed for the prediction of bridge scour. Specifically, the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation. Training of the system to new bridge geometries and flow conditions can be achieved by obtaining real time data, via novel electromagnetic sensors monitoring scour depth. Once the model is trained with data representative of the new system, bridge scour prediction can be performed for high/design flows or floods.
Application of ANFIS to Phase Estimation for Multiple Phase Shift Keying
NASA Technical Reports Server (NTRS)
Drake, Jeffrey T.; Prasad, Nadipuram R.
2000-01-01
The paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating phase in Multiple Phase Shift Keying (M-PSK) modulation. A brief overview of communications phase estimation is provided. The modeling of both general open-loop, and closed-loop phase estimation schemes for M-PSK symbols with unknown structure are discussed. Preliminary performance results from simulation of the above schemes are presented.
Soft computing modeling for indirect determination of the weathering degrees of a granitic rock
NASA Astrophysics Data System (ADS)
Dagdelenler, G.; Sezer, E.; Gokceoglu, C.
2010-05-01
Determination of weathering degrees of intact rock has been one of the difficult problems in engineering geology. Additionally, granitic rocks are commonly used as building and ornamental stones and pavement material in various civil engineering structures. For this reason, correct determination of weathering degree of the granitic rocks has a crucial importance in engineering geology. Up to now, some approaches for the determination of weathering degree of granitic rocks have been proposed. Some soft computing methods have been used for the determination of the weathering degree of the granitic rocks. However, in literature, the adaptive neuro-fuzzy inference system has not been used for the weathering classification yet. For this reason, the main purpose of the present study is to apply some soft computing methods such as artificial neural networks and adaptive neuro-fuzzy inference system on the determination of weathering degree of a granitic rock selected from Turkey by using some index and mechanical properties. The study is formed by four main stages such as sampling, testing, modeling and assessment of the model performances. During the modeling stage, two weathering prediction models with multi-inputs are developed with two soft computing techniques such as artificial neural networks and the adaptive neuro-fuzzy inference system. The general performances of models developed in this study are close; however the adaptive neuro-fuzzy inference system exhibits the best performance considering the performance index and the degree of consistency. Finally, both models developed in this present study can be used when determining the weathering degree. The results obtained from this study revealed that the soft computing techniques used in the study are highly useful tools to solve some complex problems encountered frequently in engineering geology.
A multi-step predictor for dynamic system property forecasting
NASA Astrophysics Data System (ADS)
Wang, Wilson; Vrbanek, Josip, Jr.
2007-12-01
A reliable multi-step predictor is very useful to a wide array of industries to forecast the behavior of dynamic systems. In this paper, an adaptive predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm to forecast properties of dynamic systems. An online training technique is proposed to improve forecasting convergence and accommodate different operating conditions. The viability of the developed predictor is firstly evaluated based on benchmark data sets, and then it is implemented for real-time machinery system monitoring. The monitoring index is derived from measurement based on a beta kurtosis reference function. The investigation results show that the developed adaptive predictor is a reliable forecasting tool and is able to accommodate different system conditions. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. Its performance is superior to other classical forecasting schemes.
Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference
Bai; Hsiao; Tsai; Lin
2000-01-01
An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance. PMID:10641641
The Role of Probability-Based Inference in an Intelligent Tutoring System.
ERIC Educational Resources Information Center
Mislevy, Robert J.; Gitomer, Drew H.
Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…
NASA Astrophysics Data System (ADS)
Jontof-Hutter, Daniel; Van Laerhoven, Christa L.; Ford, Eric B.
2016-05-01
Hundreds of multi-transiting systems discovered by the Kepler mission show Transit Timing Variations (TTV). In cases where the TTVs are uniquely attributable to transiting planets, the TTVs enable precise measurements of planetary masses and orbital parameters. Of particular interest are the constraints on eccentricity vectors that can be inferred in systems of low-mass exoplanets.The TTVs in these systems are dominated by a signal caused by near-resonant mean motions. This causes the well-known near-degeneracy between planetary masses and orbital eccentricities. In addition, it causes a degeneracy between the eccentricities of interacting planet pairs.For many systems, the magnitude of individual eccentricities are weakly constrained, yet the data typically provide a tight constraint on the posterior joint distribution for the eccentricity vector components. This permits tight constraints on the relative eccentricity and degree of alignment of interacting planets.For a sample of two and three-planet systems with TTVs, we highlight the effects of these correlations. While the most eccentric orbital solutions for these systems show apsidal alignment, this is often due to the degeneracy that causes correlated constraints on the eccentricity vector components. We compare the likelihood of apsidal alignment for two choices of eccentricity prior: a wide prior using a Rayleigh distribution of scale length 0.1 and a narrower prior with scale length 0.02. In all cases the narrower prior decreased the fraction of samples that exhibited apsidal alignment. However, apsidal alignment persisted in the majority of cases with a narrower eccentricity prior. For a sample of our TTV solutions, we ran simulations of these systems over secular timescales, and decomposed their eccentricity eigenmodes over time, confirming that in most cases, the eccentricities were dominated by parallel eigenmodes which favor apsidal alignment.
Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling
Pullen, Nick; Morris, Richard J.
2014-01-01
Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focusses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design. PMID:24523891
NASA Astrophysics Data System (ADS)
Wilting, Jens; Lehnertz, Klaus
2015-08-01
We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.
The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection
NASA Astrophysics Data System (ADS)
Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti
2014-06-01
In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.
Crop parameters estimation by fuzzy inference system using X-band scatterometer data
NASA Astrophysics Data System (ADS)
Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.
2013-03-01
Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.
1985-01-01
This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.
Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.
Azar, Ahmad Taher
2013-09-01
Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679
Application of Soft Computing in Coherent Communications Phase Synchronization
NASA Technical Reports Server (NTRS)
Drake, Jeffrey T.; Prasad, Nadipuram R.
2000-01-01
The use of soft computing techniques in coherent communications phase synchronization provides an alternative to analytical or hard computing methods. This paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for phase synchronization in coherent communications systems utilizing Multiple Phase Shift Keying (MPSK) modulation. A brief overview of the M-PSK digital communications bandpass modulation technique is presented and it's requisite need for phase synchronization is discussed. We briefly describe the hybrid platform developed by Jang that incorporates fuzzy/neural structures namely the, Adaptive Neuro-Fuzzy Interference Systems (ANFIS). We then discuss application of ANFIS to phase estimation for M-PSK. The modeling of both explicit, and implicit phase estimation schemes for M-PSK symbols with unknown structure are discussed. Performance results from simulation of the above scheme is presented.
A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease.
Camara, Carmen; Warwick, Kevin; Bruña, Ricardo; Aziz, Tipu; del Pozo, Francisco; Maestú, Fernando
2015-11-01
Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7% in 70% of the patients. PMID:26385550
Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse
2014-01-01
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets. PMID:25295291
Bal, Mert; Amasyali, M. Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse
2014-01-01
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets. PMID:25295291
Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS).
Velayutham, C Shunmuga; Kumar, Satish
2005-01-01
This paper presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them. PMID:15732396
Dragović, Ivana; Turajlić, Nina; Pilčević, Dejan; Petrović, Bratislav; Radojević, Dragan
2015-01-01
Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand. PMID:27069500
Du, Yi-Chun; Lin, Chia-Hung
2012-02-01
Detecting lower limb peripheral vascular occlusive disease (PVOD) early is important for patients to prevent disabling claudication, ischaemic rest pain and gangrene. According to previous research, the pulse timing and shape distortion characteristics of photoplethysmography (PPG) signals tend to increase with disease severity and calibrated amplitude decreases with vascular diseases. However, this is not a reliable method of evaluating the condition of PVOD because of noise effect. In this paper, an adaptive network-based fuzzy inference system (ANFIS) is proposed to assess lower limb PVOD based on PPG signals. PPG signals are non-invasively recorded from the right and left sides at the big toe sites from twenty subjects, including normal condition (Nor), lower-grade disease (LG), and higher-grade disease (HG) groups. The number of each group is 10, 8 and 2 respectively, and the ages ranged from 24 to 65 years. With the time-domain technique, the parameters for the absolute bilateral differences (right-to-left side of foot) in pulse delay and amplitude were extracted for analyzing ANFIS. The results indicated that ANFIS based on three timing parameters base bilateral differences, including ΔPTTf and ΔPTTp, and ΔRT has a high rate and noise tolerance of PVOD assessment. PMID:20703718
Tfwala, Samkele S.; Wang, Yu-Min; Lin, Yu-Chieh
2013-01-01
Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R2 of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM. PMID:24453876
User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.
1988-01-01
Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.
NASA Astrophysics Data System (ADS)
Narayana Rao, T.; Radhakrishna, B.; Srivastava, Rohit; Mohan Satyanarayana, T.; Narayana Rao, D.; Ramesh, R.
2008-05-01
An attempt has been made, for the first time, to effectively utilize the synergy of various approaches providing microphysical information of precipitation to study short term variations in a Mesoscale Convective System (MCS). A campaign has been conducted wherein rain samples are collected during the passage of MCSs over Gadanki, India, and simultaneously a powerful VHF radar and disdrometer have been operated to infer the characteristics of the vertical structure and rain drop size distribution (DSD) of precipitation. Besides the convection and transition rain, two distinctly different phases of the stratiform rain are identified. Evaporation of rain drops seems to be significant in both convection and stratiform portions of MCS. Observed changes in the temporal variation of the stable oxygen isotope ratios (δ18O) of precipitation are interpreted in terms of microphysical processes leading to isotopic fractionation. The pattern of variability in isotopic abundance is found to be different from convection to transition and to stratiform rain. The present analysis clearly shows that the height (or temperature) and the rain regime of condensation are of paramount importance in determining δ18O. Correlations of δ18O with rainfall integral parameters stress the need for caution in interpreting the depleted isotopic ratios are due to high rainfall and/or bigger drops.
A Weather Forecasting System using concept of Soft Computing: a new approach
NASA Astrophysics Data System (ADS)
Sharma, A.; Datta, U.
2007-07-01
Weather forecasting and warnings are the major services provided by the meteorological profession. Many government and private agencies are working on its behavior but still it is challenging and incomplete. We propose a new technique to construct the learning set of images, which represents the actual data. We relate this data to the forthcoming weather events based on their previous records and history or whatever recognized by our system. Our work presents a new approach where the data explanation is performed with soft computing technique i.e. a neuro-fuzzy system is used to predict meteorological position on the basis of measurements by a weather system designed by us. This model can help us in making forecast of different weather conditions like rain and thunderstorm, sunshine and dry day, and perhaps a cloudy weather i.e. purpose of this model is to represent a warning system for likely adverse conditions. Our model is designed with a concept of Adaptive Forecasting Model(AFD) in the mind. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain weather conditions. The results are compared with actual working of meteorological department and these results confirm that our model which is based on soft computing have the potential for successful application to weather forecasting. We considered atmospheric pressure a primary key parameter and atmospheric temperature and relative humidity secondary type. We will, however, examine temperature as signature of weather conditions in some typical cases where we could observe the effect of temperature. As an example, the estimations produced by the proposed methodology were applied on different weather forecasting data provided by the Gwalior meteorology center to make the result more practical and believable. The forecasts are always current are based on neuro-fuzzy systems that utilize the concept of soft computing. Real time processing of weather data indicate that the neuro-fuzzy based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.
Kimura, S; Araki, D; Matsumura, K; Okada-Hatakeyama, M
2012-02-01
Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments. PMID:22155075
Error modeling of DEMs from topographic surveys of rivers using fuzzy inference systems
NASA Astrophysics Data System (ADS)
Bangen, Sara; Hensleigh, James; McHugh, Peter; Wheaton, Joseph
2016-02-01
Digital elevation models (DEMs) have become common place in the earth sciences as a tool to characterize surface topography and set modeling boundary conditions. All DEMs have a degree of inherent error that is propagated to subsequent models and analyses. While previous research has shown that DEM error is spatially variable it is often represented as spatially uniform for analytical simplicity. Fuzzy inference systems (FIS) offer a tractable approach for modeling spatially variable DEM error, including flexibility in the number of inputs and calibration of outputs based on survey technique and modeling environment. We compare three FIS error models for DEMs derived from TS surveys of wadeable streams and test them at 34 sites in the Columbia River basin. The models differ in complexity regarding the number/type of inputs and degree of site-specific parameterization. A 2-input FIS uses inputs derived from the topographic point cloud (slope, point density). A 4-input FIS adds interpolation error and 3-D point quality. The 5-input FIS adds bed-surface roughness estimates. Both the 4 and 5-input FIS model output were parameterized to site-specific values. In the wetted channel we found (i) the 5-input FIS resulted in lower mean δz due to including roughness, and (ii) the 4 and 5-input FIS resulted in a higher standard deviation and maximum δz due to the inclusion of site-specific bank heights. All three FIS gave plausible estimates of DEM error, with the two more complicated models offering an improvement in the ability to detect spatially localized areas of DEM uncertainty.
NASA Astrophysics Data System (ADS)
Soto, I.; Andréfouët, S.; Hu, C.; Muller-Karger, F. E.; Wall, C. C.; Sheng, J.; Hatcher, B. G.
2009-06-01
Ocean color images acquired from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) from 1998 to 2006 were used to examine the patterns of physical connectivity between land and reefs, and among reefs in the Mesoamerican Barrier Reef System (MBRS) in the northwestern Caribbean Sea. Connectivity was inferred by tracking surface water features in weekly climatologies and a time series of weekly mean chlorophyll- a concentrations derived from satellite imagery. Frequency of spatial connections between 17 pre-defined, geomorphological domains that include the major reefs in the MBRS and river deltas in Honduras and Nicaragua were recorded and tabulated as percentage of connections. The 9-year time series of 466 weekly mean images portrays clearly the seasonal patterns of connectivity, including river plumes and transitions in the aftermath of perturbations such as hurricanes. River plumes extended offshore from the Honduras coast to the Bay Islands (Utila, Cayo Cochinos, Guanaja, and Roatán) in 70% of the weekly mean images. Belizean reefs, especially those in the southern section of the barrier reef and Glovers Atoll, were also affected by riverine discharges in every one of the 9 years. Glovers Atoll was exposed to river plumes originating in Honduras 104/466 times (22%) during this period. Plumes from eastern Honduras went as far as Banco Chinchorro and Cozumel in Mexico. Chinchorro appeared to be more frequently connected to Turneffe Atoll and Honduran rivers than with Glovers and Lighthouse Atolls, despite their geographic proximity. This new satellite data analysis provides long-term, quantitative assessments of the main pathways of connectivity in the region. The percentage of connections can be used to validate predictions made using other approaches such as numerical modeling, and provides valuable information to ecosystem-based management in coral reef provinces.
A multi-step predictor with a variable input pattern for system state forecasting
NASA Astrophysics Data System (ADS)
Liu, Jie; Wang, Wilson; Golnaraghi, Farid
2009-07-01
A reliable predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive multi-step predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm for system state forecasting. A variable input pattern is proposed to improve the forecasting performance. A hybrid training algorithm, based on the recursive Levenberg-Marquardt algorithm and recursive least square estimate, is suggested to enhance forecasting convergence and to accommodate time-varying system conditions. The viability of the developed predictor is evaluated by simulations on both benchmark data sets and experimental data sets corresponding to machinery condition monitoring. The investigation results show that the developed adaptive predictor is a reliable and robust multi-step forecasting tool. It can capture and track system's response quickly and accurately. It outperforms other related classical forecasting schemes.
GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.
Tung, W L; Quek, C; Cheng, P
2004-05-01
Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging. PMID:15109685
NASA Astrophysics Data System (ADS)
Caticha, Ariel
2011-03-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.
Kanno, Y.; Vokoun, J.C.; Letcher, B.H.
2011-01-01
Brook trout Salvelinus fontinalis populations have declined in much of the native range in eastern North America and populations are typically relegated to small headwater streams in Connecticut, USA. We used sibship reconstruction to infer mating systems, dispersal and effective population size of resident (non-anadromous) brook trout in two headwater stream channel networks in Connecticut. Brook trout were captured via backpack electrofishing using spatially continuous sampling in the two headwaters (channel network lengths of 4.4 and 7.7 km). Eight microsatellite loci were genotyped in a total of 740 individuals (80-140 mm) subsampled in a stratified random design from all 50 m-reaches in which trout were captured. Sibship reconstruction indicated that males and females were both mostly polygamous although single pair matings were also inferred. Breeder sex ratio was inferred to be nearly 1:1. Few large-sized fullsib families (>3 individuals) were inferred and the majority of individuals were inferred to have no fullsibs among those fish genotyped (family size = 1). The median stream channel distance between pairs of individuals belonging to the same large-sized fullsib families (>3 individuals) was 100 m (range: 0-1,850 m) and 250 m (range: 0-2,350 m) in the two study sites, indicating limited dispersal at least for the size class of individuals analyzed. Using a sibship assignment method, the effective population size for the two streams was estimated at 91 (95%CI: 67-123) and 210 (95%CI: 172-259), corresponding to the ratio of effective-to-census population size of 0.06 and 0.12, respectively. Both-sex polygamy, low variation in reproductive success, and a balanced sex ratio may help maintain genetic diversity of brook trout populations with small breeder sizes persisting in headwater channel networks. ?? 2010 Springer Science+Business Media B.V.
Robson, Barry
2007-08-01
What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining and biomedical analytics, as in QM. PMID:17608401
Discrimination of Human Forearm Motions on the Basis of Myoelectric Signals by Using Adaptive Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Kiso, Atsushi; Seki, Hirokazu
This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.
Measure of librarian pressure using fuzzy inference system: A case study in Longyan University
NASA Astrophysics Data System (ADS)
Huang, Jian-Jing
2014-10-01
As the hierarchy of middle managers in college's librarian. They may own much work pressure from their mind. How to adapt psychological problem, control the emotion and keep a good relationship in their work place, it becomes an important issue. Especially, they work in China mainland environment. How estimate the librarians work pressure and improve the quality of service in college libraries. Those are another serious issues. In this article, the authors would like discuss how can we use fuzzy inference to test librarian work pressure.
NASA Astrophysics Data System (ADS)
Mun, Johnathan; de Albuquerque, Nelson R.; Liong, Choong-Yeun; Salleh, Abdul Razak
2013-04-01
This paper presents the ASKE-Risk method, coupled with Fuzzy Inference Systems, and Monte Carlo Risk Simulation to measure and prioritize Individual Technical Competence of a value chain to assess changes in the human capital of a company. ASKE is an extension of the method Knowledge Value Added, which proposes the use of a proxy variable for measuring the flow of knowledge used in a key process, creating a relationship between the company's financial results and the resources used in each of the business processes.
NASA Astrophysics Data System (ADS)
Pricop, Emil; Zamfir, Florin; Paraschiv, Nicolae
2015-11-01
Process control is a challenging research topic for both academia and industry for a long time. Controllers evolved from the classical SISO approach to modern fuzzy or neuro-fuzzy embedded devices with networking capabilities, however PID algorithms are still used in the most industrial control loops. In this paper, we focus on the implementation of a PID controller using mbed NXP LPC1768 development board. This board integrates a powerful ARM Cortex- M3 core and has networking capabilities. The implemented controller can be remotely operated by using an Internet connection and a standard Web browser. The main advantages of the proposed embedded system are customizability, easy operation and very low power consumption. The experimental results obtained by using a simulated process are analysed and shows that the implementation can be done with success in industrial applications.
Modeling substorm current systems using conductivity distributions inferred from DE auroral images
NASA Technical Reports Server (NTRS)
Kamide, Y.; Craven, J. D.; Frank, L. A.; Ahn, B.-H.; Akasofu, S.-I.
1986-01-01
The first attempt to systematically use DE 1 auroral images to infer ionospheric conductivities is presented. These conductivities are then used to compute the distributions of ionospheric and field-aligned current patterns during auroral substorms. It is concluded that the western electrojet is, in general, collocated with the region of high auroral luminosity, while the region of relatively low luminosity in the evening sector is collocated with the eastward electrojet. The upward field-aligned currents exist in the brightest auroral region on the poleward side of the evening auroral oval and on the equatorward side of the morning oval. A significant amount of ionospheric currents can flow in regions where there are no bright auroral emissions.
San, Phyo Phyo; Ling, Sai Ho; Nguyen, Hung T
2012-01-01
Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. PMID:23367375
Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam
2016-05-01
Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. PMID:26914521
Hydrological connectivity inferred from diatom transport through the riparian-stream system
NASA Astrophysics Data System (ADS)
Martínez-Carreras, N.; Wetzel, C. E.; Frentress, J.; Ector, L.; McDonnell, J. J.; Hoffmann, L.; Pfister, L.
2015-07-01
Diatoms (Bacillariophyta) are one of the most common and diverse algal groups (ca. 200 000 species, ≈ 10-200 μm, unicellular, eukaryotic). Here we investigate the potential of aerial diatoms (i.e. diatoms nearly exclusively occurring outside water bodies, in wet, moist or temporarily dry places) to infer surface hydrological connectivity between hillslope-riparian-stream (HRS) landscape units during storm runoff events. We present data from the Weierbach catchment (0.45 km2, northwestern Luxembourg) that quantify the relative abundance of aerial diatom species on hillslopes and in riparian zones (i.e. surface soils, litter, bryophytes and vegetation) and within streams (i.e. stream water, epilithon and epipelon). We tested the hypothesis that different diatom species assemblages inhabit specific moisture domains of the catchment (i.e. HRS units) and, consequently, the presence of certain species assemblages in the stream during runoff events offers the potential for recording whether there was hydrological connectivity between these domains or not. We found that a higher percentage of aerial diatom species was present in samples collected from the riparian and hillslope zones than inside the stream. However, diatoms were absent on hillslopes covered by dry litter and the quantities of diatoms (in absolute numbers) were small in the rest of hillslope samples. This limits their use for inferring hillslope-riparian zone connectivity. Our results also showed that aerial diatom abundance in the stream increased systematically during all sampled events (n = 11, 2011-2012) in response to incident precipitation and increasing discharge. This transport of aerial diatoms during events suggested a rapid connectivity between the soil surface and the stream. Diatom transport data were compared to two-component hydrograph separation, and end-member mixing analysis (EMMA) using stream water chemistry and stable isotope data. Hillslope overland flow was insignificant during most sampled events. This research suggests that diatoms were likely sourced exclusively from the riparian zone, since it was not only the largest aerial diatom reservoir, but also since soil water from the riparian zone was a major streamflow source during rainfall events under both wet and dry antecedent conditions. In comparison to other tracer methods, diatoms require taxonomy knowledge and a rather large processing time. However, they can provide unequivocal evidence of hydrological connectivity and potentially be used at larger catchment scales.
Novichkov, Pavel S.; Rodionov, Dmitry A.; Stavrovskaya, Elena D.; Novichkova, Elena S.; Kazakov, Alexey E.; Gelfand, Mikhail S.; Arkin, Adam P.; Mironov, Andrey A.; Dubchak, Inna
2010-05-26
RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.
Yang, Bin; Zhang, Wei; Wang, Haifeng; Song, Chuandong; Chen, Yuehui
2016-05-01
Regulatory interactions among target genes and regulatory factors occur instantaneously or with time-delay. In this paper, we propose a novel approach namely TDSDMI based on time-delayed S-system model (TDSS) model and delayed mutual information (DMI) to infer time-delay gene regulatory network (TDGRN). Firstly DMI is proposed to delete redundant regulator factors for each target gene. Secondly restricted gene expression programming (RGEP) is proposed as a new representation of the TDSS model to identify instantaneous and time-delayed interactions. To verify the effectiveness of the proposed method, TDSDMI is applied to both simulated and real biological datasets. Experimental results reveal that TDSDMI performs better than the recent reconstruction methods. PMID:27058285
Goldstein, D B; Roemer, G W; Smith, D A; Reich, D E; Bergman, A; Wayne, R K
1999-01-01
To assess the reliability of genetic markers it is important to compare inferences that are based on them to a priori expectations. In this article we present an analysis of microsatellite variation within and among populations of island foxes (Urocyon littoralis) on California's Channel Islands. We first show that microsatellite variation at a moderate number of loci (19) can provide an essentially perfect description of the boundaries between populations and an accurate representation of their historical relationships. We also show that the pattern of variation across unlinked microsatellite loci can be used to test whether population size has been constant or increasing. Application of these approaches to the island fox system indicates that microsatellite variation may carry considerably more information about population history than is currently being used. PMID:9927470
Developing a Dynamic Inference Expert System to Support Individual Learning at Work
ERIC Educational Resources Information Center
Hung, Yu Hsin; Lin, Chun Fu; Chang, Ray I.
2015-01-01
In response to the rapid growth of information in recent decades, knowledge-based systems have become an essential tool for organizational learning. The application of electronic performance-support systems in learning activities has attracted considerable attention from researchers. Nevertheless, the vast, ever-increasing amount of information is…
NASA Astrophysics Data System (ADS)
Murray, J. R.; Svarc, J. L.; Pollitz, F. F.; Floyd, M.; Funning, G.; Johanson, I. A.
2014-12-01
Tectonic ground deformation due to the 24 August 2014 M6 South Napa earthquake was recorded by continuous GPS (CGPS) sites of the Plate Boundary Observatory, Bay Area Regional Deformation, and USGS networks. Additionally, survey-mode GPS (SGPS) measurements were carried out following the event to densify the spatial coverage and record postseismic deformation. We compare earthquake offsets estimated using two sets of time series for the same sites, one with position estimates at five minute intervals and the other at one day intervals. On average the offset magnitudes from the five-minute positions are ~70% those estimated from the daily data, demonstrating that substantial postseismic deformation occurred immediately following the coseismic slip. Fitting the daily position time series for sites within ~35 km of the epicenter with a combination of coseismic offset and a logarithmic decay that begins immediately following the event indicates that cumulative displacement from 25 August 2014 to 24 September 2014 is on average ~70% of the estimated displacement on 24 August at these sites. While earthquakes on creeping faults of the San Andreas system have often generated postseismic displacement of similar magnitude to the coseismic, the mapped trace associated with this earthquake was not known to creep. Using the coseismic offsets estimated from the five-minute solutions and a Bayesian inference approach, the most likely planar fault that passes through the epicenter and intersects the Earth's surface is vertical and strikes 155o, in good agreement with seismic moment tensor estimates. The peak GPS-inferred coseismic slip extends ~12 km northwest and up-dip of the hypocenter. Initial postseismic slip models derived from GPS data show shallow afterslip near and to the southeast of the inferred coseismic slip; the afterslip is generally shallower and southeast of the zone of aftershocks. However, the resulting GPS residuals exhibit more complex spatial patterns that are not well-fit with a simple planar geometry. Further collection and analysis of postseismic SGPS data, in combination with other geodetic observations, will help to characterize the postseismic deformation source process, its temporal evolution, and its relation to aftershocks.
NASA Astrophysics Data System (ADS)
Christensen, Claire Petra
Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author's own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject. The review-like Chapters 1, 2, and 4 are intended to convey the co-evolution of network science and the slow but noticeable breakdown of boundaries between disciplines in academia as research and comparison of diverse systems has brought to light the shared properties of these systems. It is the author's hope that theses chapters impart some sense of the remarkable and rapid progress in complex systems research that has led to this unprecedented academic synergy. Chapters 3 and 5 detail the author's original work in the context of complex systems research. Chapter 3 presents the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. These networks are then analyzed from a graph theoretical perspective, and their biological viability is critiqued by comparing the networks' graph theoretical properties to those of other biological systems. The results of topological perturbation analyses revealing commonalities in behavior at multiple levels of complexity are also presented, and are shown to be an invaluable means by which to ascertain the level of complexity to which the network inference process is robust to noise. Chapter 5 outlines a learning algorithm for the development of a realistic, evolving social network (a city) into which a disease is introduced. The results of simulations in populations spanning two orders of magnitude are compared to prevaccine era measles data for England and Wales and demonstrate that the simulations are able to capture the quantitative and qualitative features of epidemics in populations as small as 10,000 people. The work presented in Chapter 5 validates the utility of network simulation in concurrently probing contact network dynamics and disease dynamics.
Magnetospheric current systems as inferred from SYM and ASY mid-latitude indices
NASA Astrophysics Data System (ADS)
Ganushkina, Natalia; Dubyagin, Stepan
2015-04-01
Separating the contributions from different current systems from point magnetic field measurements and interpreting them as belonging to one system or another is very difficult, and caution must be used when deciphering near-Earth currents from either data or modeling results. At the same time, there are other continuously measured quantities, which can provide, though indirectly, information about the dynamics of the magnetospheric current systems. The SYM-H and ASY-H indices, computed from the observations of magnetic field at low latitude ground-based stations, contain contributions from major magnetospheric current systems, such as the symmetric and asymmetric ring current, tail current, magnetopause currents and field-aligned currents. Highly distorted magnetospheric magnetic field during storm times due to disturbances in the current systems is reflected in the SYM-H and ASY-H observed variations. Using empirical magnetospheric models we study the relative contribution from different current systems to the SYM and ASY mid-latitude indices. It was found that the models can reproduce ground based mid-latitude indices rather well. The good agreement between the indices computed using magnetospheric models and real ones indicates that purely ionospheric current systems, on average, give modest contribution to these indices. The superposed epoch analysis of the indices computed using the models shows that the cross-tail current gives dominant contribution to SYM-H index during the main phase though this contribution can not be separated from FAC region 2 and partial ring current contributions since these systems are overlapped. The relative contribution from symmetric ring current to SYM-H starts to increase a bit prior or just after SYM-H minimum and attains its maximum during recovery phase. The ASY-H and ASY-D indices are controlled by interplay between three current systems which close via the ionosphere. The region 1 FAC gives the largest contribution to ASY-H and ASY-D indices during the main phase, though, region 2 FAC and partial ring current contributions are also prominent. The partial ring current is the main contributor to the ASY indices during the recovery phase. In addition, we discuss the application of these results to resolving the long-debated inconsistencies of the substorm-controlled geomagnetic storm scenario.
Inference in dynamic systems using B-splines and quasilinearized ODE penalties.
Frasso, Gianluca; Jaeger, Jonathan; Lambert, Philippe
2016-05-01
Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems. We propose a smoothing approach regularized by a quasilinearized ODE-based penalty. Within the quasilinearized spline-based framework, the estimation reduces to a conditionally linear problem for the optimization of the spline coefficients. Furthermore, standard ODE compliance parameter(s) selection criteria are applicable. We evaluate the performances of the proposed strategy through simulated and real data examples. Simulation studies suggest that the proposed procedure ensures more accurate estimates than standard nonlinear least squares approaches when the state (initial and/or boundary) conditions are not known. PMID:26602190
NASA Astrophysics Data System (ADS)
Villasante-Marcos, Víctor; Finizola, Anthony; Barde-Cabusson, Stéphanie; López, Carmen; Di Gangi, Fabio; Levieux, Guillaume; Morin, Julie; Ricci, Tullio; Schütze, Claudia; Suski-Ricci, Barbara
2014-05-01
An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.
NASA Astrophysics Data System (ADS)
Villasante-Marcos, Víctor; Finizola, Anthony; Abella, Rafael; Barde-Cabusson, Stéphanie; Blanco, María José; Brenes, Beatriz; Cabrera, Víctor; Casas, Benito; De Agustín, Pablo; Di Gangi, Fabio; Domínguez, Itahiza; García, Olaya; Gomis, Almudena; Guzmán, Juan; Iribarren, Ilazkiñe; Levieux, Guillaume; López, Carmen; Luengo-Oroz, Natividad; Martín, Isidoro; Moreno, Manuel; Meletlidis, Stavros; Morin, Julie; Moure, David; Pereda, Jorge; Ricci, Tullio; Romero, Enrique; Schütze, Claudia; Suski-Ricci, Barbara; Torres, Pedro; Trigo, Patricia
2014-02-01
An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~ 237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.
NASA Astrophysics Data System (ADS)
Winter, Steven John
Methodology development was conducted to incorporate a modular knowledge-base representation into an expert system engineering design application. The objective for using multidisciplinary methodologies in defining a design system was to develop a system framework that would be applicable to a wide range of engineering applications. The technique of "knowledge clustering" was used to construct a general decision tree for all factual information relating to the design application. This construction combined the design process surface knowledge and specific application depth knowledge. Utilization of both levels of knowledge created a system capable of processing multiple controlling tasks including; organizing factual information relative to the cognitive levels of the design process, building finite element models for depth knowledge analysis, developing a standardized finite element code for parallel processing, and determining a best solution generated by design optimization procedures. Proof of concept for the methodology developed here is shown in the implementation of an application defining the analysis and optimization of a composite aircraft canard subjected to a general compound loading condition. This application contained a wide range of factual information and heuristic rules. The analysis tools used included a finite element (FE) processor and numerical optimizer. An advisory knowledge-base was also developed to provide a standard for conversion of serial FE code for parallel processing. All knowledge-bases developed operated as either an advisory, selection, or classification systems. Laminate properties are limited to even-numbered, quasi-isotropic ply stacking sequences. This retained full influence of the coupled in-plane and bending effects of the structures behavior. The canard is modeled as a constant thickness plate and discretized into a varying number of four or nine-noded, quadrilateral, shear-deformable plate elements. The benefit gained by a designer from using this design methodology is presented by examining the capability of the system to satisfy the different levels of engineering design cognitive abilities. Numerical results of design iterations are provided to detail the expert system's advise in feasible region identification, and multiple iteration outcomes are used to justify solution assessment rules used in controlling the optimization process.
Truyens, Simon; Arbo, Maria M; Shore, Joel S
2005-10-01
Turnera provides a useful system for exploring two significant evolutionary phenomena-shifts in breeding system (distyly vs. homostyly) and the evolution of polyploids. To explore these, the first molecular phylogeny of Turnera was constructed using sequences of the internal transcribed spacer region (ITS) of nuclear ribosomal DNA for 37 taxa. We attempted to resolve the origins of allopolyploid species using single-strand conformation polymorphism and sequencing of homeologous copies of ITS. Two allohexaploid species possessed putative ITS homeologues (T. velutina and T. orientalis). A phylogenetic analysis to identify progenitors contributing to the origins of these polyploids was unsuccessful, possibly as a result of concerted evolution of ITS. Breeding system evolution was mapped onto the phylogeny assuming distyly to be ancestral in Turnera. Self-compatible homostyly appears to have arisen independently at least three times in Turnera; however, we were not able to determine whether there have been independent origins of homostyly among hexaploid species in series Turnera. Our phylogenetic analyses suggest that series Turnera is monophyletic. Neither series Microphyllae nor Anomalae, however, appear to be monophyletic. Future taxonomic revisions may require new circumscriptions of these latter series. PMID:21646092
NASA Astrophysics Data System (ADS)
Hidaka, Hiroshi; Ohta, Yohei; Yoneda, Shigekazu
2003-09-01
The Ba isotopic composition of primitive meteorites is one of the more useful tracers to study nucleosynthetic processes in the early solar system, because the seven stable Ba isotopes consist of p-, r-, and s-process nucleosynthetic components and possible decay products from presently extinct 135Cs. Ba isotopic analyses were performed on acid leachates from five carbonaceous chondrites, Murchison (CM2), Sayama (CM2), Allende (CV3), Isuna (CO3) and Maralinga (CK4), and one basaltic achondrite, Juvinas (Eucrite). The Ba isotopic data of acid treatment fractions in carbonaceous chondrites suggest the presence of independent nucleosynthetic components for s- and r-processes in the solar system. The isotopic patterns from the acid residual fractions of two CM2 meteorites, Murchison and Sayama, show an enrichment of s-process isotopes, which is similar to that of presolar SiC grains. On the other hand, their acid leachates have an excess of r-process components among the Ba isotopes. In many cases of carbonaceous chondrites other than CM meteorites, the excess of r-process isotopes is too small for a detailed discussion about the nucleosynthetic contribution. This may be interpreted as metamorphic destruction of presolar grains in meteorite matrices associated with petrographic-type meteorites. Juvinas shows no isotopic anomalies of Ba, because the isotopic record in the early solar system was reset possibly by magmatic processes in the parent body.
Methane leakage from evolving petroleum systems: Masses, rates and inferences for climate feedback
NASA Astrophysics Data System (ADS)
Berbesi, L. A.; di Primio, R.; Anka, Z.; Horsfield, B.; Wilkes, H.
2014-02-01
The immense mass of organic carbon contained in sedimentary systems, currently estimated at 1.56×1010 Tg (Des Marais et al., 1992), bears the potential of affecting global climate through the release of thermally or biologically generated methane to the atmosphere. Here we investigate the potential of naturally-occurring gas leakage, controlled by petroleum generation and degradation as a forcing mechanism for climate at geologic time scales. We addressed the potential methane contributions to the atmosphere during the evolution of petroleum systems in two different, petroliferous geological settings: the Western Canada Sedimentary Basin (WCSB) and the Central Graben area of the North Sea. Besides 3D numerical simulation, different types of mass balance and theoretical approaches were applied depending on the data available and the processes taking place in each basin. In the case of the WCSB, we estimate maximum thermogenic methane leakage rates in the order of 10-2-10-3 Tg/yr, and maximum biogenic methane generation rates of 10-2 Tg/yr. In the case of the Central Graben, maximum estimates for thermogenic methane leakage are in the order in 10-3 Tg/yr. Extrapolation of our results to a global scale suggests that, at least as a single process, thermal gas generation in hydrocarbon kitchen areas would not be able to influence climate, although it may contribute to a positive feedback. Conversely, only the sudden release of subsurface methane accumulations, formed over geologic timescales, can possibly allow for petroleum systems to exert an effect on climate.
Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.
2013-01-01
Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902
NASA Astrophysics Data System (ADS)
Abouelmagd, A.; McCabe, M. F.; Castro, M. C.; Sultan, M.; Jana, R. B.; Al-Mashharawi, S.
2014-12-01
One of the most valuable groundwater reserves in Saudi Arabia is the Saq aquifer system (SAS), a thick (400-1200 meters) sandstone unit that extends across 300,000 km2 in Saudi Arabia and neighboring Jordan. Due to its high productivity and high water quality, current pumping and overexploitation of the aquifer has significantly lowered the groundwater level over the years. Understanding the recharge regimes of the SAS is critical for the development of sustainable exploitation of water resources in the region and for the establishment of appropriate management practices. In this study, we investigate the hydrologic setting of the SAS and seek to differentiate the degree of paleo versus modern contributions using a range of geochemical approaches. Multiple groundwater samples were collected from deep production wells tapping the SAS at depths between 375-1800 m and across a range of locations. Samples were analyzed for their chemical concentrations, stable isotopic compositions (δ18O and δ2H), and dissolved noble gas concentrations and isotopic ratios. Examining these data identifies unmixed pools of fossil groundwater at deeper depths as well as mixed shallower systems that indicate contributions from modern precipitation. Through isotopic and noble gas analyses, the relative age and timing of these recharge events was examined and show contributions from both glacial and inter-glacial periods, with some modest contributions from modern meteoric sources.
Macdonald, Benn; Husmeier, Dirk
2015-01-01
Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem in contemporary systems biology. Conventional methods involve repeatedly solving the ODEs by numerical integration, which is computationally onerous and does not scale up to complex systems. Aimed at reducing the computational costs, new concepts based on gradient matching have recently been proposed in the computational statistics and machine learning literature. In a preliminary smoothing step, the time series data are interpolated; then, in a second step, the parameters of the ODEs are optimized, so as to minimize some metric measuring the difference between the slopes of the tangents to the interpolants, and the time derivatives from the ODEs. In this way, the ODEs never have to be solved explicitly. This review provides a concise methodological overview of the current state-of-the-art methods for gradient matching in ODEs, followed by an empirical comparative evaluation based on a set of widely used and representative benchmark data. PMID:26636071
Macdonald, Benn; Husmeier, Dirk
2015-01-01
Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem in contemporary systems biology. Conventional methods involve repeatedly solving the ODEs by numerical integration, which is computationally onerous and does not scale up to complex systems. Aimed at reducing the computational costs, new concepts based on gradient matching have recently been proposed in the computational statistics and machine learning literature. In a preliminary smoothing step, the time series data are interpolated; then, in a second step, the parameters of the ODEs are optimized, so as to minimize some metric measuring the difference between the slopes of the tangents to the interpolants, and the time derivatives from the ODEs. In this way, the ODEs never have to be solved explicitly. This review provides a concise methodological overview of the current state-of-the-art methods for gradient matching in ODEs, followed by an empirical comparative evaluation based on a set of widely used and representative benchmark data. PMID:26636071
Image haze removal using a hybrid of fuzzy inference system and weighted estimation
NASA Astrophysics Data System (ADS)
Wang, Jyun-Guo; Tai, Shen-Chuan; Lin, Cheng-Jian
2015-05-01
The attenuation of the light transmitted through air can reduce image quality when taking a photograph outdoors, especially in a hazy environment. Hazy images often lack sufficient information for image recognition systems to operate effectively. In order to solve the aforementioned problems, this study proposes a hybrid method combining fuzzy theory with weighted estimation for the removal of haze from images. A transmission map is first created based on fuzzy theory. According to the transmission map, the proposed method automatically finds the possible atmospheric lights and refines the atmospheric lights by mixing these candidates. Weighted estimation is then employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Experimental results demonstrate the superiority of the proposed method over existing methods with regard to contrast, color depth, and the elimination of halo artifacts.
Davies, Andrew J; Hope, Max J
2015-07-15
Contingency plans are essential in guiding the response to marine oil spills. However, they are written before the pollution event occurs so must contain some degree of assumption and prediction and hence may be unsuitable for a real incident when it occurs. The use of Bayesian networks in ecology, environmental management, oil spill contingency planning and post-incident analysis is reviewed and analysed to establish their suitability for use as real-time environmental decision support systems during an oil spill response. It is demonstrated that Bayesian networks are appropriate for facilitating the re-assessment and re-validation of contingency plans following pollutant release, thus helping ensure that the optimum response strategy is adopted. This can minimise the possibility of sub-optimal response strategies causing additional environmental and socioeconomic damage beyond the original pollution event. PMID:26006775
Network-assisted crop systems genetics: network inference and integrative analysis.
Lee, Tak; Kim, Hyojin; Lee, Insuk
2015-04-01
Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years. PMID:25698380
Radchenko, O A
2015-11-01
Based on an analysis of sequence variation in mitochondrial and nuclear markers, the levels of divergence, relationships, and system of the suborder Zoarcoidei was defined. It was demonstrated that DNA lineages of the families Bathymasteridae and Cebidichthyidae were positioned at the bottom ofthe suborder phylogenetic tree. The family Zoarcidae is a monophyletic group, the youngest in the evolutionary terms. Zoarcidae, Anarhichadidae, Neozorcidae, and Eulophiidae form a group of related families. The family Stichaeidae is heterogeneous and has a polyphyletic origin; within this family, the subfamilies Chirolophinae, Alectgiinae, Xiphisterinae, and Stichaeinae are sister taxa. The subfamilies Opisthocentrinae and Lumpeninae are isolated from Stichaedae; Opisthocentrinae is closely associated with the families Pholidae and Ptilichthyidae, and Lumpeninae is closely associated with Zaproridae and Cryptacanthodidae. It is suggested that the rank of subfamilies Opisthocentrinae and Lumpeninae should be raised. PMID:26845857
Wang, Jun; Zhou, Xiaobo; Li, Fuhai; Bradley, Pamela L.; Chang, Shih-Fu; Perrimon, Norbert; Wong, Stephen T.C.
2009-01-01
With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation. We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling. We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes. PMID:18547870
NASA Astrophysics Data System (ADS)
Mohamed, A.; Sultan, M.; Ahmed, M.; Yan, E.
2014-12-01
The Nubian Sandstone Aquifer System (NSAS) is shared by Egypt, Libya, Chad and Sudanand is one of the largest (area: ~ 2 × 106 km2) groundwater systems in the world. Despite its importance to the population of these countries, major hydrological parameters such as modern recharge and extraction rates remain poorly investigated given: (1) the large extent of the NSAS, (2) the absence of comprehensive monitoring networks, (3) the general inaccessibility of many of the NSAS regions, (4) difficulties in collecting background information, largely included in unpublished governmental reports, and (5) limited local funding to support the construction of monitoring networks and/or collection of field and background datasets. Data from monthly Gravity Recovery and Climate Experiment (GRACE) gravity solutions were processed (Gaussian smoothed: 100 km; rescaled) and used to quantify the modern recharge to the NSAS during the period from January 2003 to December 2012. To isolate the groundwater component in GRACE data, the soil moisture and river channel storages were removed using the outputs from the most recent Community Land Model version 4.5 (CLM4.5). GRACE-derived recharge calculations were performed over the southern NSAS outcrops (area: 835 × 103 km2) in Sudan and Chad that receive average annual precipitation of 65 km3 (77.5 mm). GRACE-derived recharge rates were estimated at 2.79 ± 0.98 km3/yr (3.34 ± 1.17 mm/yr). If we take into account the total annual extraction rates (~ 0.4 km3; CEDARE, 2002) from Chad and Sudan the average annual recharge rate for the NSAS could reach up to ~ 3.20 ± 1.18 km3/yr (3.84 ± 1.42 mm/yr). Our recharge rates estimates are similar to those calculated using (1) groundwater flow modelling in the Central Sudan Rift Basins (4-8 mm/yr; Abdalla, 2008), (2) WaterGAP global scale groundwater recharge model (< 5 mm/yr, Döll and Fiedler, 2008), and (3) chloride tracer in Sudan (3.05 mm/yr; Edmunds et al. 1988). Given the available global coverage of the temporal GRACE solutions for the past twelve years and plans are underway for the deployment of a GRACE follow-On and GRACE-II missions, we suggest that within the next few years, GRACE will probably become the most practical, informative, and cost-effective tool for monitoring the recharge of large aquifers across the globe.
NASA Technical Reports Server (NTRS)
Harrison, P. Ann
1993-01-01
All the NASA VEGetation Workbench (VEG) goals except the Learning System provide the scientist with several different techniques. When VEG is run, rules assist the scientist in selecting the best of the available techniques to apply to the sample of cover type data being studied. The techniques are stored in the VEG knowledge base. The design and implementation of an interface that allows the scientist to add new techniques to VEG without assistance from the developer were completed. A new interface that enables the scientist to add techniques to VEG without assistance from the developer was designed and implemented. This interface does not require the scientist to have a thorough knowledge of Knowledge Engineering Environment (KEE) by Intellicorp or a detailed knowledge of the structure of VEG. The interface prompts the scientist to enter the required information about the new technique. It prompts the scientist to enter the required Common Lisp functions for executing the technique and the left hand side of the rule that causes the technique to be selected. A template for each function and rule and detailed instructions about the arguments of the functions, the values they should return, and the format of the rule are displayed. Checks are made to ensure that the required data were entered, the functions compiled correctly, and the rule parsed correctly before the new technique is stored. The additional techniques are stored separately from the VEG knowledge base. When the VEG knowledge base is loaded, the additional techniques are not normally loaded. The interface allows the scientist the option of adding all the previously defined new techniques before running VEG. When the techniques are added, the required units to store the additional techniques are created automatically in the correct places in the VEG knowledge base. The methods file containing the functions required by the additional techniques is loaded. New rule units are created to store the new rules. The interface that allow the scientist to select which techniques to use is updated automatically to include the new techniques. Task H was completed. The interface that allows the scientist to add techniques to VEG was implemented and comprehensively tested. The Common Lisp code for the Add Techniques system is listed in Appendix A.
Inferring social network structure in ecological systems from spatio-temporal data streams
Psorakis, Ioannis; Roberts, Stephen J.; Rezek, Iead; Sheldon, Ben C.
2012-01-01
We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 106 points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents. PMID:22696481
Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems
Ghattas, Omar
2013-10-15
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUARO Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.
Early accretion of protoplanets inferred from a reduced inner solar system 26Al inventory
NASA Astrophysics Data System (ADS)
Schiller, Martin; Connelly, James N.; Glad, Aslaug C.; Mikouchi, Takashi; Bizzarro, Martin
2015-06-01
The mechanisms and timescales of accretion of 10-1000 km sized planetesimals, the building blocks of planets, are not yet well understood. With planetesimal melting predominantly driven by the decay of the short-lived radionuclide 26Al (26Al→26Mg; t1/2 = 0.73 Ma), its initial abundance determines the permissible timeframe of planetesimal-scale melting and its subsequent cooling history. Currently, precise knowledge about the initial 26Al abundance [(26Al/27Al)0] exists only for the oldest known solids, calcium aluminum-rich inclusions (CAIs) - the so-called canonical value. We have determined the 26Al/27Al of three angrite meteorites, D'Orbigny, Sahara 99555 and NWA 1670, at their time of crystallization, which corresponds to (3.98 ± 0.15) ×10-7, (3.64 ± 0.18) ×10-7, and (5.92 ± 0.59) ×10-7, respectively. Combined with a newly determined absolute U-corrected Pb-Pb age for NWA 1670 of 4564.39 ± 0.24 Ma and published U-corrected Pb-Pb ages for the other two angrites, this allows us to calculate an initial (26Al/27Al)0 of (1.33-0.18+0.21) ×10-5 for the angrite parent body (APB) precursor material at the time of CAI formation, a value four times lower than the accepted canonical value of 5.25 ×10-5. Based on their similar 54Cr/52Cr ratios, most inner solar system materials likely accreted from material containing a similar 26Al/27Al ratio as the APB precursor at the time of CAI formation. To satisfy the abundant evidence for widespread planetesimal differentiation, the subcanonical 26Al budget requires that differentiated planetesimals, and hence protoplanets, accreted rapidly within 0.25 ± 0.15 Ma of the formation of canonical CAIs.
NASA Astrophysics Data System (ADS)
Emery, A. F.; Valenti, E.; Bardot, D.
2007-01-01
Parameter estimation is generally based upon the maximum likelihood approach and often involves regularization. Typically it is desired that the results be unbiased and of minimum variance. However, it is often better to accept biased estimates that have minimum mean square error. Bayesian inference is an attractive approach that achieves this goal and incorporates regularization automatically. More importantly, it permits us to analyse experiments in which both the system response and the independent variables (time, sensor position, experimental conditions, etc) are corrupted by noise and in which the model includes nuisance variables. This paper describes the use of Bayesian inference for an apparently simple experiment which is, in fact, fundamentally difficult and is compounded by a nuisance variable. By presenting this analysis we hope that members of the inverse community will see the value of applying Bayesian inference.
Location and Geometry of the Deccan Traps Feeder System Inferred from Dike Geochemistry
NASA Astrophysics Data System (ADS)
Vanderkluysen, L.; Mahoney, J. J.; Hooper, P. R.; Sheth, H. C.
2006-12-01
Three large dike swarms are exposed in the 500,000 km2 Deccan Traps of India: the Konkan swarm, where the majority of dikes run roughly N-S, sub-parallel to the west coast; the Narmada-Tapi swarm in the north-central Deccan, where dikes dominantly trend ENE-WSW, sub-parallel to the Narmada and Tapi river valleys; and the Nasik-Pune swarm in the central western Deccan, comprising dikes that lack any strongly preferred trends. Following Hooper (Nature, 349, 246, 1990), previous workers have commonly interpreted the Nasik-Pune swarm as the principal locus of feeder dikes for the immense lava pile. The lack of a preferred trend, in turn, has been used as key evidence that the main phase of volcanism was not accompanied by significant lithospheric extension. Our study of the major and trace element and Pb, Sr, and Nd isotope characteristics of samples from all three dike swarms reveals rare dikes in the Nasik-Pune and coastal swarms with signatures matching those of the Igatpuri and Jawhar lava formations in the lowermost part of the lava sequence, and the Bushe Formation in the middle part of the sequence. The scarcity of such dikes suggests these two swarms were not the main feeder systems for the middle and lower portions of the lava pile (or that most feeders for the lower and middle formations are not exposed and lie buried by later flows). Major and trace element data for dikes of the Narmada-Tapi swarm instead suggest that feeders for the lower and middle formations may be located in this swarm. In contrast, dikes with isotopic and chemical signatures matching those of the upper group of lava formations (i.e. the Poladpur, Ambenali and Mahabaleshwar formations) are abundant in the Konkan and Nasik-Pune swarms. As a group, these feeder-like dikes do not display any obvious dominant trend. Dikes compositionally similar to those three formations have also been identified as far north as the Narmada River and as far south as Goa, some 50 km south of the southernmost exposures of flood basalt flows.
ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.
Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine
2012-04-01
Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem. PMID:21562791
Li, Xin; Li, Jing
2010-01-01
We study the haplotype inference problem from pedigree data under the zero recombination assumption, which is well supported by real data for tightly linked markers (i.e., single nucleotide polymorphisms (SNPs)) over a relatively large chromosome segment. We solve the problem in a rigorous mathematical manner by formulating genotype constraints as a linear system of inheritance variables. We then utilize disjoint-set structures to encode connectivity information among individuals, to detect constraints from genotypes, and to check consistency of constraints. On a tree pedigree without missing data, our algorithm can output a general solution as well as the number of total specific solutions in a nearly linear time O(mn · α(n)), where m is the number of loci, n is the number of individuals and α is the inverse Ackermann function4, which is a further improvement over existing ones3, 8, 12, 15. We also extend the idea to looped pedigrees and pedigrees with missing data by considering existing (partial) constraints on inheritance variables. The algorithm has been implemented in C++ and will be incorporated into our PedPhase package8. Experimental results show that it can correctly identify all 0-recombinant solutions with great efficiency. Comparisons with other two popular algorithms show that the proposed algorithm achieves 10 to 105-fold improvements over a variety of parameter settings. The experimental study also provides empirical evidences on the complexity bounds suggested by theoretical analysis. PMID:19642289
NASA Astrophysics Data System (ADS)
Daud, H.; Razali, R.; Low, T. J.; Sabdin, M.; Zafrul, S. Z. Mohd
2014-06-01
An expert system for diagnosing sickness and suggesting treatment based on Islamic Medication (IM) was constructed using Rule Based (RB) and Bayes' theorem (BT) algorithms independently as its inference engine. Comparative assessment on the quality of diagnosing based on symptoms provided by users for certain type of sickness using RB and BT reasoning that lead to the suggested treatment (based on IM) are discussed. Both approaches are found to be useful, each has its own advantages and disadvantages. Major difference of the two algorithms is the selection of symptoms during the diagnosing process. For BT, likely combinations of symptoms need to be classified for each sickness before the diagnosing process. This eliminates any irrelevant sickness based on the combination of symptoms provided by user and combination of symptoms that is unlikely. This is not the case for RB, it will diagnose the sickness as long as one the symptoms is related to the sickness regardless of unlikely combination. Few tests have been carried out using combinations of symptoms for same sickness to investigate their diagnosing accuracy in percentage. BT gives more promising diagnosing results compared to RB for each sickness that comes with common symptoms.
NASA Astrophysics Data System (ADS)
Migeon, Sébastien; Mulder, Thierry; Savoye, Bruno; Sage, Françoise
2012-03-01
The Var Turbidite System (NW Mediterranean Sea) is fed during the present-day highstand sea level by large earthquake-induced ignitive turbidity currents, low-density turbidity currents resulting from retrogressive failures triggered on the upper continental slope, and hyperpycnal flows related to the Var River floods. Using a large dataset including bathymetric data, side-scan sonar images, seismic-reflection profiles, cores and photographs of the seafloor, this paper attempts to better constrain the hydrodynamic behaviour of debris flows and turbidity currents along the Upper and Middle Valley of the Var Turbidite System. The drastic change of the seafloor morphology between the Upper and the Middle Valley suggests that gravity flows undergo rapid transformation from cohesive to fully turbulent behaviour. This transformation is related to a hydraulic jump caused by an abrupt decrease in slope angle at the transition between the Upper and the Middle Valley and is associated with en masse deposition and elevation of the seafloor. Strong seafloor erosion prevails in the Middle Valley, suggesting that, for a low and constant slope angle, turbulent flows must regain a balance between concentration and flow thickness rapidly after they experience hydraulic jump. The internal stratification and vertical grain-size distribution within turbulent flows are inferred from the distribution of fine- to coarse-grained turbidites found in cores located along the crest of the Var Sedimentary Ridge with a decreasing elevation above the floor of the Middle Valley. The theoretical vertical velocity profile deduced from the vertical grain-size distribution exhibits a general trend and an inflection of the gradient curve different from those of the velocity profiles classically obtained using numerical modelling.
Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien
2013-01-01
Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023
Dehlaghi, Vahab; Taghipour, Mostafa; Haghparast, Abbas; Roshani, Gholam Hossein; Rezaei, Abbas; Shayesteh, Sajjad Pashootan; Adineh-Vand, Ayoub; Karimi, Gholam Reza
2015-01-01
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D0), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy. PMID:25498836
Dehlaghi, Vahab; Taghipour, Mostafa; Haghparast, Abbas; Roshani, Gholam Hossein; Rezaei, Abbas; Shayesteh, Sajjad Pashootan; Adineh-Vand, Ayoub; Karimi, Gholam Reza
2015-04-01
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D{sub 0}), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy.
Adaptive fuzzy system for 3-D vision
NASA Technical Reports Server (NTRS)
Mitra, Sunanda
1993-01-01
An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.
NASA Astrophysics Data System (ADS)
Minami, S.; Iguchi, M.; Mikada, H.; Goto, T.; Takekawa, J.
2010-12-01
We studied the ground deformation associated with the eruption at the Showa crater of Sakurajima, which has been active since 2006. Using the Mogi’s spherical pressure model, a volume change of magma chambers can be estimated from the displacement, tilt, or strain observations near the ground surface. After the application of the Mogi’s model to data in the past observations, the existence of two magma chambers has been inferred beneath the Sakurajima down to a depth of 5 km. The tilt and strain data in 2 underground tunnel sites observed 36 hours before an eruption in April 9, 2009, are analyzed to reveal the behavior of magma leading to eruption. From these data, there seems to be a time lag in the inflation between the two magma chambers at a depth of 4km and 0.1km, respectively. In addition, the order of the volume change of the shallow source is about one tenth of that of the deep one. A system which consists of shallow and deep magma chambers and a vertical conduit connecting them is numerically modeled to investigate the mechanism of the time lag and why the difference in the magnitude of the volumetric changes in the two chambers appears as described above. The initial values of magma properties in the deep magma chamber are assumed from the volcanic ejecta of Sakurajima volcano. We assumed that magma is supplied with a constant rate to the deep magma chamber. The two different pressure limits are assigned to the deep and shallow chamber, respectively: (1) one triggers the magma uprise from the deep to the shallow, and (2) the other to start to erupt. In a one-dimentional steady flow model of a magma conduit, we consider the vesiculation of volatile-bearing magma, gas escape and overpressure in the bubble due to the viscous resistance, which largely influences the physical properties of magma. Although our simulation results cannot exactly describe the data, we confirmed that our hypothetical model with two triggers could explain the time lag of the inflation and the difference in the order of the volume change. We would like to propose that the numerical simulation could be a powerful tool for understanding the behavior of magma before eruption once ground deformations are well observed.
NASA Astrophysics Data System (ADS)
Moron, Vincent; Barbero, Renaud; Robertson, Andrew W.
2015-07-01
Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill score. In general, ANFIS models show superior results in terms of correlation coefficient for the overall case study. As a pioneer study, it is proposed that ANFIS is a promising tool for the purpose of seasonal predictions in Australia as they produce comparable accuracy using minimal inputs, require less development time and they are less complex compared to dynamic models.
NASA Astrophysics Data System (ADS)
Mekanik, F.; Imteaz, M. A.; Talei, A.
2016-05-01
Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill score. In general, ANFIS models show superior results in terms of correlation coefficient for the overall case study. As a pioneer study, it is proposed that ANFIS is a promising tool for the purpose of seasonal predictions in Australia as they produce comparable accuracy using minimal inputs, require less development time and they are less complex compared to dynamic models.
Intent inference for attack aircraft through fusion
NASA Astrophysics Data System (ADS)
Ng, Gee Wah; Ng, Khin Hua; Yang, Rong; Foo, Pek Hui
2006-04-01
Intent inference is about analyzing the actions and activities of an adversarial force or target of interest to reach a conclusion (prediction) on its purpose. In this paper, we report one of our research works on intent inference to determine the likelihood of an attack aircraft being tracked by a military surveillance system delivering its weapon. Effective intent inference will greatly enhance the defense capability of a military force in taking preemptive action against potential adversaries. It serves as early warning and assists the commander in his decision making. For an air defense system, the ability to accurately infer the likelihood of a weapon delivery by an attack aircraft is critical. It is also important for an intent inference system to be able to provide timely inference. We propose a solution based on the analysis of flight profiles for offset pop-up delivery. Simulation tests are carried out on flight profiles generated using different combinations of delivery parameters. In each simulation test, the state vectors of the tracked aircraft are updated via the application of the Interacting Multiple Model filter. Relevant variables of the filtered track (flight trajectory) are used as inputs to a Mamdani-type fuzzy inference system. The output produced by the fuzzy inference system is the inferred possibility of the tracked aircraft carrying out a pop-up delivery. We present experimental results to support our claim that the proposed solution is indeed feasible and also provides timely inference that will assist in the decision making cycle.
ERIC Educational Resources Information Center
Finson, Kevin D.
2010-01-01
Learning about what inferences are, and what a good inference is, will help students become more scientifically literate and better understand the nature of science in inquiry. Students in K-4 should be able to give explanations about what they investigate (NSTA 1997) and that includes doing so through inferring. This article provides some tips…
Poorbagher, Hadi; Moghaddam, Maryam Nasrollahpour; Eagderi, Soheil; Farahmand, Hamid
2016-07-01
The DNA breakage has been widely used in ecotoxicological studies to investigate effects of pesticides in fishes. The present study used a fuzzy inference system to quantify the breakage of DNA double strand in Aphanius sophiae exposed to the cypermethrin. The specimens were adapted to different temperatures and salinity for 14 days and then exposed to cypermethrin. DNA of each specimens were extracted, electrophoresed and photographed. A fuzzy system with three input variables and 27 rules were defined. The pixel value curve of DNA on each gel lane was obtained using ImageJ. The DNA breakage was quantified using the pixel value curve and fuzzy system. The defuzzified values were analyzed using a three-way analysis of variance. Cypermethrin had significant effects on DNA breakage. Fuzzy inference systems can be used as a tool to quantify the breakage of double strand DNA. DNA double strand of the gill of A. sophiae is sensitive enough to be used to detect cypermethrin in surface waters in concentrations much lower than those reported in previous studies. PMID:27000282
Inferring biotic interactions from proxies.
Morales-Castilla, Ignacio; Matias, Miguel G; Gravel, Dominique; Araújo, Miguel B
2015-06-01
Inferring biotic interactions from functional, phylogenetic and geographical proxies remains one great challenge in ecology. We propose a conceptual framework to infer the backbone of biotic interaction networks within regional species pools. First, interacting groups are identified to order links and remove forbidden interactions between species. Second, additional links are removed by examination of the geographical context in which species co-occur. Third, hypotheses are proposed to establish interaction probabilities between species. We illustrate the framework using published food-webs in terrestrial and marine systems. We conclude that preliminary descriptions of the web of life can be made by careful integration of data with theory. PMID:25922148
NASA Astrophysics Data System (ADS)
Lombardi, Ilaria; Console, Luca
In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.
Friston, Karl
2014-01-01
I greatly enjoyed Seth's compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)-which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue). These perspectives include perception as hypothesis testing, affordance, and sensorimotor contingencies. This commentary briefly rehearses the fundaments of active inference and offers a formal basis for Seth's key argument. PMID:24702520
Daily water level forecasting using wavelet decomposition and artificial intelligence techniques
NASA Astrophysics Data System (ADS)
Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.
2015-01-01
Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.
Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach
NASA Astrophysics Data System (ADS)
Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel
2016-01-01
Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.
Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting
NASA Astrophysics Data System (ADS)
Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon
In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach
ERIC Educational Resources Information Center
Fazlollahtabar, Hamed; Mahdavi, Iraj
2009-01-01
Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach
ERIC Educational Resources Information Center
Fazlollahtabar, Hamed; Mahdavi, Iraj
2009-01-01
Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The
A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller
NASA Astrophysics Data System (ADS)
Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi
This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.
NASA Astrophysics Data System (ADS)
Aizawa, Koki; Koyama, Takao; Hase, Hideaki; Uyeshima, Makoto; Kanda, Wataru; Utsugi, Mitsuru; Yoshimura, Ryokei; Yamaya, Yusuke; Hashimoto, Takeshi; Yamazaki, Ken'ichi; Komatsu, Shintaro; Watanabe, Atsushi; Miyakawa, Koji; Ogawa, Yasuo
2014-01-01
Broadband magnetotelluric (MT) measurements were conducted in 2010 and 2011 in the vicinity of Shinmoe-dake Volcano in the Kirishima volcano group, Japan, where sub-Plinian eruptions took place 3 times during 26-27 January 2011. By combining the new observations with previous MT data, it is found that an anomalous phase in excess of 90° is commonly observed in the northern sector of the Kirishima volcano group. Because the anomalous phase is not explained by 1-D or 2-D structure with isotropic resistivity media, 3-D inversions were performed. By applying small errors to the anomalous phase, we successfully estimated a 3-D resistivity structure that explains not only the normal data but also the anomalous phase data. The final model shows a vertical conductor that is located between a deep-seated conductive body (at a depth greater than 10 km) and a shallow conductive layer. By applying the findings of geophysical and petrological studies of the 2011 sub-Plinian eruptions, we infer that the subvertical conductor represents a zone of hydrothermal aqueous fluids at temperatures over 400°C, in which a magma pathway (interconnected melt) is partially and occasionally formed before magmatic eruptions. To the north of the deep conductor, earthquake swarms occurred from 1968 to 1969, suggesting that these earthquakes were caused by volcanic fluids.
Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor
NASA Astrophysics Data System (ADS)
Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.
2011-08-01
Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.
Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems
Sinčak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Virčikova, Maria; Vranayova, Zuzana; Sabol, Jakub
2014-01-01
Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475
Artificial intelligence in public health prevention of legionelosis in drinking water systems.
Sinčak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Virčikova, Maria; Vranayova, Zuzana; Sabol, Jakub
2014-08-01
Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475
Bayesian inference on proportional elections.
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Bayesian Inference on Proportional Elections
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John
2014-01-01
Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long‐term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short‐range alignment and trilateration arrays and 48 near‐fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half‐space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M≥5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic‐hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.
Nonmonotonic inference rules for multiple inheritance with exceptions
Sandewall, E.
1986-10-01
The semantics of inheritance ''hierarchies'' with multiple inheritance and exceptions is discussed, and a partial semantics in terms of a number of structure types is defined. Previously proposed inference systems for inheritance with exceptions are discussed. A new and improved inference system is proposed, using a fixed number of nonmonotonic inference rules. The hierarchy is viewed as a set of atomic propositions using the two relations isa (subsumption) and nisa (nonsubsumption). General results concerning systems of nonmonotonic inference rules can immediately be applied to the proposed inference system.
NASA Astrophysics Data System (ADS)
Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad
2014-03-01
Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.
NASA Astrophysics Data System (ADS)
Ansari, Hamid Reza
2014-09-01
In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.
NASA Technical Reports Server (NTRS)
Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)
2001-01-01
A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.
Social Inference Through Technology
NASA Astrophysics Data System (ADS)
Oulasvirta, Antti
Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.
Estimation and optimization of thermal performance of evacuated tube solar collector system
NASA Astrophysics Data System (ADS)
Dikmen, Erkan; Ayaz, Mahir; Ezen, H. Hüseyin; Küçüksille, Ecir U.; Şahin, Arzu Şencan
2014-05-01
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) in order to predict the thermal performance of evacuated tube solar collector system have been used. The experimental data for the training and testing of the networks were used. The results of ANN are compared with ANFIS in which the same data sets are used. The R2-value for the thermal performance values of collector is 0.811914 which can be considered as satisfactory. The results obtained when unknown data were presented to the networks are satisfactory and indicate that the proposed method can successfully be used for the prediction of the thermal performance of evacuated tube solar collectors. In addition, new formulations obtained from ANN are presented for the calculation of the thermal performance. The advantages of this approaches compared to the conventional methods are speed, simplicity, and the capacity of the network to learn from examples. In addition, genetic algorithm (GA) was used to maximize the thermal performance of the system. The optimum working conditions of the system were determined by the GA.
NASA Technical Reports Server (NTRS)
Jones, Patricia S.; Mitchell, Christine M.; Rubin, Kenneth S.
1988-01-01
The authors proposes an architecture for an expert system that can function as an operator's associate in the supervisory control of a complex dynamic system. Called OFMspert (operator function model (OFM) expert system), the architecture uses the operator function modeling methodology as the basis for the design. The authors put emphasis on the understanding capabilities, i.e., the intent referencing property, of an operator's associate. The authors define the generic structure of OFMspert, particularly those features that support intent inferencing. They also describe the implementation and validation of OFMspert in GT-MSOCC (Georgia Tech-Multisatellite Operations Control Center), a laboratory domain designed to support research in human-computer interaction and decision aiding in complex, dynamic systems.
Almeida, Josivanda S; Migues, Vitor H; Diniz, Débora; Affonso, Paulo Roberto A M
2015-01-01
Cytogenetic studies in Neotropical electric knifefish of genus Gymnotus have shown a remarkable interspecific variability, including distinct sex chromosome systems. In this study, we present the first chromosomal data in Gymnotus bahianus from Contas River basin, northeastern South America. Based on extensive analyses, the modal diploid values were 2n = 36 (30m/sm + 6st) for females and 2n = 37 (32m/sm + 5st) for males. Therefore, a novel XX/XY1Y2 sex chromosome system is described for the genus. Single nucleolar organizer regions (NORs) interspersed to GC-rich sites were detected on a subtelocentric pair (7th) for both sexes and confirmed by ﬂuorescent in situ hybridization with 18S rDNA probes. Heterochromatin was detected at pericentromeric regions of all chromosomes and interspersed to NORs on pair 7 and 5S rDNA cistrons on pair 9. The highly differentiated karyotype of Gymnoytus bahianus, with low diploid numbers and a unique XX/XY1Y2 system, reinforces the independent origin of sex chromosomes in Gymnotiformes and seems to reflect the particular evolutionary history of this species in a small and isolated drainage system. Moreover, in spite of morphological similarities, the present results indicate a remarkable chromosomal divergence in relation to closely related species such as G. sylvius and G. carapo. PMID:25596613
NASA Astrophysics Data System (ADS)
Li, Bin; Sørensen, Mathilde; Atakan, Kuvvet; Havskov, Jens
2015-04-01
The Shanxi rift system is one of the most outstanding intra-plate transtensional fault zones in the North China block. Earthquake focal mechanisms of the rift system are investigated for the time period 1965 - Apr. 2014. A total of 143 focal mechanisms of ML ≥ 3.0 earthquakes were compiled. Among them, 105 solutions are newly determined by combining the P-wave first motions and full waveform inversion, and 38 solutions are from available published data. Stress tensor inversion was then performed based on the new database. The results show that most solutions exhibit normal or strike-slip faulting, and the regional stress field is transtensional and dominated by NNW-SSE extension. This correlates well with results from GPS data, geological field observations and leveling measurements across the faults. Heterogeneity exists in the regional stress field, as indicated by individual stress tensor inversions conducted for five subzones. While the minimum stress axis (σ3) appears to be consistent and stable, the orientations, especially the plunges, of the maximum and intermediate stresses (σ1 and σ2) vary significantly among the different subzones. Based on our results and combining multidisciplinary observations from geological surveys, GPS and cross-fault monitoring, a kinematic model is proposed, in which the Shanxi rift system is situated between two opposite rotating blocks, exhibiting a transtensional stress regime. This model illustrates the present-day stress field and its correlation with the regional tectonics, as well as the current crustal deformation of the Shanxi rift system. Results obtained in this study, may help to understand the geodynamics, neotectonic activity, active seismicity and potential seismic hazard in this region of North China.
NASA Astrophysics Data System (ADS)
Li, Bin; Atakan, Kuvvet; Sørensen, Mathilde Bøttger; Havskov, Jens
2015-05-01
Earthquake focal mechanisms of the Shanxi rift system, North China, are investigated for the time period 1965-April 2014. A total of 143 focal mechanisms of ML ≥ 3.0 earthquakes were compiled. Among them, 105 solutions are newly determined in this study by combining the P-wave first motions and full waveform inversion, and 38 solutions are from available published data. Stress tensor inversion was then performed based on the new database. The results show that most solutions in the Shanxi rift system exhibit normal or strike-slip faulting, and the regional stress field is transtensional and dominated by NNW-SSE extension. This correlates well with results from GPS data, geological field observations and levelling measurements across the faults. Heterogeneity exists in the regional stress field, as indicated by individual stress tensor inversions conducted for five subzones. While the minimum stress axis (σ3) appears to be consistent and stable, the orientations, especially the plunges, of the maximum and intermediate stresses (σ1 and σ2) vary significantly along the strike of the different subzones. Based on our results and combining multidisciplinary observations from geological surveys, GPS and cross-fault monitoring, a kinematic model is proposed for the Shanxi rift system, in which the rift is situated between two opposite rotating crustal blocks, exhibiting a transtensional stress regimes. This model illustrates the present-day stress field and its correlation to the regional tectonics, as well as the current crustal deformation of the Shanxi rift system. Results obtained in this study, may help to understand the geodynamics, neotectonic activity, active seismicity and potential seismic hazard in this region.
The CO2 system in the Mediterranean Sea inferred from a 3D coupled physical-biogeochemical model
NASA Astrophysics Data System (ADS)
Ulses, Caroline; Kessouri, Fayçal; Estournel, Claude; Marsaleix, Patrick; Beuvier, Jonathan; Somot, Samuel; Touratier, Frank; Goyet, Catherine; Coppola, Laurent; Diamond, Emilie; Metzl, Nicolas
2015-04-01
The semi-enclosed Mediterranean Sea characterized by short residence times is considered as a region particularly sensitive to natural and anthropogenic forcing. Due to scarce CO2 measurements in the whole basin, the CO2 system, for instance the air-sea CO2 exchanges and the effects of the increase of atmospheric CO2, are poorly characterized. 3D physical-biogeochemical coupled models are unique tools that can provide integrated view and gain understanding in the temporal and spatial variation of the CO2 system variables (dissolved inorganic carbon, total alkalinity, partial pressure of CO2 and pH). An extended version of the biogeochemical model Eco3m-S (Auger et al., 2014), that describes the cycles of carbon, nitrogen, phosphorus and silica, was forced by a regional circulation model (Beuvier et al., 2012) to investigate the CO2 system in the Mediterranean Sea over a 13-years period (2001-2013). First, the quality of the modelling was evaluated through comparisons with satellite and in situ observations collected in the whole basin over the study period (Touratier and Goyet, 2009; 2011 ; Rivaro et al., 2010 ; Pujo-Pay et al., 2011 ; Alvarez et al, 2014). The model reasonably reproduced the various biological regimes (north-western phytoplanctonic bloom regime, oligotrophic eastern regime, etc.) as well as the recorded spatial distribution and temporal variations of the carbonate system variables. The coupled model was then used to estimate the air-sea pCO2 exchanges and the transport of DIC and TA towards the Atlantic Ocean at the Strait of Gibraltar.
Zimmer, Christoph; Sahle, Sven
2015-10-01
Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the 'multiple shooting for stochastic systems (MSS)' method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least-squares parameter estimation methods. PMID:26405142
NASA Astrophysics Data System (ADS)
Ozbulut, Osman E.; Hurlebaus, Stefan
2009-03-01
This paper proposes a neuro-fuzzy model of NiTi shape memory alloy (SMA) wires that is capable of capturing behavior of superelastic SMAs at different temperatures and at various loading rates while remaining simple enough to realize numerical simulations. First, in order to collect data, uniaxial tensile tests are conducted on superelastic wires in the temperature range of 0 ÂºC to 40 ÂºC, and at the loading frequencies of 0.05 Hz to 2 Hz that is the range of interest for seismic applications. Then, an adaptive neuro-fuzzy inference system (ANFIS) is employed to construct a model of SMAs based on experimental input-output data pairs. The fuzzy model obtained from ANFIS training is validated by using an experimental data set that is not used during training. Upon having a model that can represent behavior of superelastic SMAs at various ambient temperature and loading-rates, nonlinear simulation of a multi-span continuous bridge isolated by rubber bearings that is equipped with SMA dampers is carried out. Response of the bridge to a historical earthquake record is presented at different ambient temperatures in order to evaluate the effect of temperature on the performance of the structure. It is shown that SMA damping elements can effectively decrease peak deck displacement and the relative displacement between piers and superstructure in an isolated bridge while recovering all the deformations to their original position.
NASA Astrophysics Data System (ADS)
Karamitopoulos, P.; Weltje, G.; Dalman, R.
2011-12-01
Spatial and temporal variability of sediment storage in fluvio-deltaic sedimentary systems is controlled by the interplay of allogenic and autogenic processes. In order to investigate the effects of this interplay on the resulting stratigraphy at varying spatio-temporal scales, we carried out a series of numerical experiments using an aggregated process-based model of fluvio-deltaic systems (SIMCLAST), which combines diffusive and advective transport with sub-grid channel stability algorithms in the fluvial domain. New distributary channels occur by avulsions under conditions of local superelevation or through bifurcations due to mouth bar deposition. A series of numerical experiments were performed under forcing by glacio-eustatic sealevel cycles in the order of 100 kyr. Initial conditions of all experiments are represented by classic continental-margin topography with a shelf break. In this scenario, erosional features (canyons) are developed when sea level falls below the shelf break. Sediment supply and liquid discharge remain constant throughout the experiments. In order to characterize the topographic variability during the experiments, we used a difference measure obtained by summation of local changes in net sediment accumulation rates across the entire model domain. Long-term average variability (10 kyr resolution) correlates strongly with the allogenic sea-level signal. The long-term variability reaches a maximum around the time interval corresponding to isochronous maximum flooding surfaces, when retrogradation gives way to a new episode of progradation. Long-term mean variability is lowest during periods of sea-level fall, when incision restricts sediment dispersal. Increasing the time resolution of our difference measure allows recognition of numerous small peaks which correspond to local changes in sediment accumulation rates induced by autogenic processes (avulsions and bifurcations). The amplitudes of these peaks are related to the rate of change of the allogenic sea-level signal. During intervals characterized by positive rates of sea-level change, retrogradational stratal patterns are generated in which avulsions are the dominant autogenic control on spatial variability. During intervals characterized by negative rates of sea-level change, progradational stratal patterns are generated in which bifurcations are the dominant autogenic control on spatial variability. The highest amplitudes of the high-resolution difference measure occur during sea-level rise, because avulsions affect the entire downstream portion of the sediment dispersal system, whereas bifurcations affect only the terminal parts of the system. These results indicate that the relation between autogenic and allogenic processes varies throughout a base-level cycle, and underscores the fact that the relative importance and the type of autogenic processes occurring in fluvio-deltaic systems are governed by allogenic (low-frequency) forcing.
NASA Astrophysics Data System (ADS)
Nimalsiri, Thusitha Bandara; Suriyaarachchi, Nuwan Buddhika; Hobbs, Bruce; Manzella, Adele; Fonseka, Morrel; Dharmagunawardena, H. A.; Subasinghe, Nalaka Deepal
2015-06-01
First comprehensive geothermal exploration in Sri Lanka was conducted in 2010 encompassing seven thermal springs, of which Kapurella records the highest temperature. The study consisted of passive magnetotelluric (MT) soundings, in which static shifts were corrected using time domain electromagnetic method (TDEM). A frequency range of 12,500-0.001 Hz was used for MT acquisition and polar diagrams were employed for dimensionality determination. MT and TDEM data were jointly inverted and 2D models were created using both transverse electric and transverse magnetic modes. A conductive southeast dipping structure is revealed from both phase pseudosections and the preferred 2D inversion model. A conductive formation starting at a depth of 7.5 km shows a direct link with the dipping structure. We suggest that these conductive structures are accounted for deep circulation and accumulation of groundwater. Our results show the geothermal reservoir of Kapurella system with a lateral extension of around 2.5 km and a depth range of 3 km. It is further found that the associated dolerite dike is not the source of heat although it could be acting as an impermeable barrier to form the reservoir. The results have indicated the location of the deep reservoir and the possible fluid path of the Kapurella system, which could be utilized to direct future geothermal studies. This pioneering study makes suggestions to improve future MT data acquisition and to use boreholes and other geophysical methods to improve the investigation of structures at depth.
NASA Astrophysics Data System (ADS)
Partsinevelos, Panagiotis; Kallimani, Christina; Tripolitsiotis, Achilleas
2015-06-01
Rockfall incidents affect civil security and hamper the sustainable growth of hard to access mountainous areas due to casualties, injuries and infrastructure loss. Rockfall occurrences cannot be easily prevented, whereas previous studies for rockfall multiple sensor early detection systems have focused on large scale incidents. However, even a single rock may cause the loss of a human life along transportation routes thus, it is highly important to establish methods for the early detection of small-scale rockfall incidents. Terrestrial photogrammetric techniques are prone to a series of errors leading to false alarm incidents, including vegetation, wind, and non relevant change in the scene under consideration. In this study, photogrammetric monitoring of rockfall prone slopes is established and the resulting multi-temporal change imagery is processed in order to minimize false alarm incidents. Integration of remote sensing imagery analysis techniques is hereby applied to enhance early detection of a rockfall. Experimental data demonstrated that an operational system able to identify a 10-cm rock movement within a 10% false alarm rate is technically feasible.
"Groundwater ages" of the Lake Chad multi-layer aquifers system inferred from 14C and 36Cl data
NASA Astrophysics Data System (ADS)
Bouchez, Camille; Deschamps, Pierre; Goncalves, Julio; Hamelin, Bruno; Seidel, Jean-Luc; Doumnang, Jean-Claude
2014-05-01
Assessment of recharge, paleo-recharge and groundwater residence time of aquifer systems of the Sahel is pivotal for a sustainable management of this vulnerable resource. Due to its stratified aquifer system, the Lake Chad Basin (LCB) offers the opportunity to assess recharge processes over time and to link climate and hydrology in the Sahel. Located in north-central Africa at the fringe between the Sahel and the Sahara, the lake Chad basin (LCB) is an endorheic basin of 2,5.106 km2. With a monsoon climate, the majority of the rainfall occurs in the southern one third of the basin, the Chari/Logone River system transporting about 90% of the runoff generated within the drainage basin. A complex multi-layer aquifer system is located in the central part of the LCB. The Quaternary unconfined aquifer, covering 500 000 km2, is characterized by the occurrence of poorly understood piezometric depressions. Artesian groundwaters are found in the Plio-Pleistocene lacustrine and deltaic sedimentary aquifers (early Pliocene and Continental Terminal). The present-day lake is in hydraulic contact with the Quaternary Aquifer, but during past megalake phases, most of the Quaternary aquifer was submerged and may experience major recharge events. To identify active recharge area and assess groundwater dynamics, one hundred surface and groundwater samples of all layers have been collected over the southern part of the LCB. Major and trace elements have been analyzed. Measurements of 36Cl have been carried out at CEREGE, on the French 5 MV AMS National Facility ASTER and 14C activities have been analyzed for 17 samples on the French AMS ARTEMIS. Additionally, the stable isotopic composition was measured on the artesian aquifer samples. In the Quaternary aquifer, results show a large scatter with waters having very different isotopic and geochemical signature. In its southern part and in the vicinity of the surface waters, groundwaters are predominantly Ca-Mg-HCO3 type waters with very high 36Cl/Cl ratio (>1000.10-15 at/at) very likely linked to the bomb pulse. These high 36Cl/Cl ratios are in the same order than the 36Cl/Cl signature of surface waters active modern recharge in this area. In the other part of the Quaternary Aquifer, waters are Na-HCO3-SO4-Cl type and are characterized by lower 36Cl/Cl ratios (around 200.10-15 at/at), suggesting longer residence time of the groundwaters. The 14C contents of the unconfined aquifer waters are all above 50 pmc, suggesting recent or Holocene recharge of this system. In contrast, the confined aquifer has a more homogeneous geochemical signature. The 14C contents are below all 0.5 pmc and mainly below detection level. 36Cl/Cl ratios are
NASA Astrophysics Data System (ADS)
Detrez, F.; Castelnau, O.; Cordier, P.; Merkel, S.; Raterron, P.
2015-10-01
Polycrystalline aggregates lacking four independent systems for the glide of dislocations can deform in a purely viscoplastic regime only if additional deformation mechanisms (such as grain boundary sliding and diffusion) are activated. We introduce an implementation of the self-consistent scheme in which this additional physical mechanism, considered as a stress relaxation mechanism, is represented by a nonlinear isotropic viscoplastic potential. Several nonlinear extensions of the self-consistent scheme, including the second-order method of Ponte-Castañeda, are used to provide an estimate of the effective viscoplastic behavior of such polycrystals. The implementation of the method includes an approximation of the isotropic potential to ensure convergence of the attractive fixed-point numerical algorithm. The method is then applied to olivine polycrystals, the main constituent of the Earth's upper mantle. Due to the extreme local anisotropy of the local constitutive behavior and the subsequent intraphase stress and strain-rate field heterogeneities, the second-order method is the only extension providing qualitative and quantitative accurate results. The effective viscosity is strongly dependent on the strength of the relaxation mechanism. For olivine, a linear viscous relaxation (e.g. diffusion) could be relevant; in that case, the polycrystal stress sensitivity is reduced compared to that of dislocation glide, and the most active slip system is not necessarily the one with the smallest reference stress due to stress concentrations. This study reveals the significant importance of the strength and stress sensitivity of the additional relaxation mechanism for the rheology and lattice preferred orientation in such highly anisotropic polycrystalline aggregates.
Linguistic Markers of Inference Generation While Reading.
Clinton, Virginia; Carlson, Sarah E; Seipel, Ben
2016-06-01
Words can be informative linguistic markers of psychological constructs. The purpose of this study is to examine associations between word use and the process of making meaningful connections to a text while reading (i.e., inference generation). To achieve this purpose, think-aloud data from third-fifth grade students ([Formula: see text]) reading narrative texts were hand-coded for inferences. These data were also processed with a computer text analysis tool, Linguistic Inquiry and Word Count, for percentages of word use in the following categories: cognitive mechanism words, nonfluencies, and nine types of function words. Findings indicate that cognitive mechanisms were an independent, positive predictor of connections to background knowledge (i.e., elaborative inference generation) and nonfluencies were an independent, negative predictor of connections within the text (i.e., bridging inference generation). Function words did not provide unique variance towards predicting inference generation. These findings are discussed in the context of a cognitive reflection model and the differences between bridging and elaborative inference generation. In addition, potential practical implications for intelligent tutoring systems and computer-based methods of inference identification are presented. PMID:25833811
Cortical circuits for perceptual inference
Friston, Karl; Kiebel, Stefan
2009-01-01
This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs. PMID:19635656
Cortical circuits for perceptual inference.
Friston, Karl; Kiebel, Stefan
2009-10-01
This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs. PMID:19635656
NASA Astrophysics Data System (ADS)
Konstantinou, K. I.; Rontogianni, S.; Lin, C.-H.
2012-04-01
The Tatun Volcano Group (TVG) is located in northern Taiwan near the capital Taipei. In this study we selected and analyzed almost four years (2004 - 2007) of its seismic activity. The seismic network established around TVG initially consisted of eight three component seismic stations with this number increasing to twelve by 2007. Local seismicity mainly involved High Frequency (HF) earthquakes occurring as isolated events or as spasmodic bursts. Mixed and Low Frequency (LF) events were observed during the same period but more rarely. During the analysis we estimated the magnitudes for the HF earthquakes and used a probabilistic non-linear method to locate all these events. We examined the temporal and spatial distribution of our data-set for each year and the monthly seismic energy distribution. In addition, complex frequencies for LF events were analyzed with the Sompi method. We juxtapose these results with gas geochemistry studies of fumaroles covering a similar period. A model for the volcano-hydrothermal system is proposed where fluids and magmatic gases ascend from a magma body that lies at around 7- 8 km depth. The movement of fluids to shallow depths increases the heat, the fracturing and also creates resonance and vibrations in cracks and conduits. This detailed analysis and previous physical volcanology observations at TVG suggest that the region is volcanically active and that measures to mitigate the risks have to be considered by the local authorities.
NASA Astrophysics Data System (ADS)
Koch, Stephan; Kuvshinov, Alexey
2015-12-01
We study if and how the South Atlantic Anomaly influences the ionospheric solar quiet (Sq) current system. Geomagnetically quiet days are processed for the years 1990 and 2011, and the Sq foci tracks are analyzed. The two datasets allow to investigate the influence of the observatory network and the solar activity on the Sq source determination. The computed tracks result in pronounced bands in the northern and southern hemisphere, which seem to neither follow the geographic nor the geomagnetic or dip equator. Remarkably, we observe a distinct scattering of the tracks over the South Atlantic Anomaly. This systematic scattering is due to a larger shift of the southern hemisphere focus northwards during the northern summer solstice and southwards during the southern summer solstice. The physical mechanism of this systematic effect remains unclear. The longitudinal variations of the Sq foci are believed to have their origin from an influence of non-migrating tides as reported in recent studies and the anomalous weak amplitude of the geomagnetic main field over the South Atlantic Anomaly.
NASA Astrophysics Data System (ADS)
Colombani, N.; Di Giuseppe, D.; Faccini, B.; Ferretti, G.; Mastrocicco, M.; Coltorti, M.
2016-06-01
Shallow lenses in reclaimed coastal areas are precious sources of freshwater for crop development, but their seasonal behaviour is seldom known in tile-drained fields. In this study, field monitoring and numerical modelling provide a robust conceptual model of these complex environments. Crop and meteorological data are used to implement an unsaturated flow model to reconstruct daily recharge. Groundwater fluxes and salinity, water table elevation, tile-drains' discharge and salinity are used to calibrate a 2D density-dependent numerical model to quantify non-reactive solute transport within the aquifer-aquitard system. Results suggest that lateral fluxes in low hydraulic conductivity sediments are limited, while water table fluctuation is significant. The use of depth-integrated monitoring to calibrate the model results in poor efficiency, while multi-level soil profiles are crucial to define the mixing zone between fresh and brackish groundwater. Measured fluxes and chloride concentrations from tile-drains not fully compare with calculated ones due to preferential flow through cracks.
Symbolic transfer entropy: inferring directionality in biosignals.
Staniek, Matthäus; Lehnertz, Klaus
2009-12-01
Inferring directional interactions from biosignals is of crucial importance to improve understanding of dynamical interdependences underlying various physiological and pathophysiological conditions. We here present symbolic transfer entropy as a robust measure to infer the direction of interactions between multidimensional dynamical systems. We demonstrate its performance in quantifying driver-responder relationships in a network of coupled nonlinear oscillators and in the human epileptic brain. PMID:19938889
NASA Technical Reports Server (NTRS)
1993-01-01
All the options in the NASA VEGetation Workbench (VEG) make use of a database of historical cover types. This database contains results from experiments by scientists on a wide variety of different cover types. The learning system uses the database to provide positive and negative training examples of classes that enable it to learn distinguishing features between classes of vegetation. All the other VEG options use the database to estimate the error bounds involved in the results obtained when various analysis techniques are applied to the sample of cover type data that is being studied. In the previous version of VEG, the historical cover type database was stored as part of the VEG knowledge base. This database was removed from the knowledge base. It is now stored as a series of flat files that are external to VEG. An interface between VEG and these files was provided. The interface allows the user to select which files of historical data to use. The files are then read, and the data are stored in Knowledge Engineering Environment (KEE) units using the same organization of units as in the previous version of VEG. The interface also allows the user to delete some or all of the historical database units from VEG and load new historical data from a file. This report summarizes the use of the historical cover type database in VEG. It then describes the new interface to the files containing the historical data. It describes minor changes that were made to VEG to enable the externally stored database to be used. Test runs to test the operation of the new interface and also to test the operation of VEG using historical data loaded from external files are described. Task F was completed. A Sun cartridge tape containing the KEE and Common Lisp code for the new interface and the modified version of the VEG knowledge base was delivered to the NASA GSFC technical representative.
NASA Astrophysics Data System (ADS)
Bui, T.; Pohlman, J.; Lapham, L.; Riedel, M.; Wing, B. A.
2010-12-01
The flux of methane from gas hydrate bearing seeps in the marine environment is partially mitigated by the anaerobic oxidation of methane coupled with sulfate reduction. Sedimentary porewater sulfate profiles above gas hydrate deposits are frequently used to estimate the efficacy of this important microbial biofilter. However, to differentiate how other processes (e.g., sulfate reduction coupled to organic matter oxidation, sulfide re-oxidation and sulfur disproportionation) affect sulfate profiles, a complete accounting of the sulfur cycle is necessary. To this end, we have obtained the first ever measurements of minor sulfur isotopic ratios (33S/32S, 36S/32S), in conjunction with the more commonly measured 34S -32S ratio, from porewater sulfate above a gas hydrate-bearing seep. Characteristic minor isotopic fractionations, even when major isotopic fractionations are similar in magnitude, help to quantify the contributions of different microbial processes to the overall sulfur cycling in the system. Down to sediment depths of 1.5 to 4 meters, the δ34S values of porewater sulfate generally increased in association with a decrease in sulfate concentrations as would be expected for active sulfate reduction. Of greater interest, covariance between the δ34S values and measured minor isotopic fractionation suggests sulfide reoxidation and sulfur disproportionation are important components of the local sulfur cycle. We hypothesize that sulfide reoxidation is coupled to redox processes involving Fe(III) and Mn(IV) reduction and that the reoxidized forms of sulfur are available for additional methane oxidation. Recognizing that sulfate reduction is only one of several microbial processes controlling sulfate profiles challenges current paradigms for interpreting sulfate profiles and may alter our understanding of methane oxidation at gas hydrate-bearing seeps.
2011-01-01
Background Main waterfowl migration systems are well understood through ringing activities. However, in mallards (Anas platyrhynchos) ringing studies suggest deviations from general migratory trends and traditions in waterfowl. Furthermore, surprisingly little is known about the population genetic structure of mallards, and studying it may yield insight into the spread of diseases such as Avian Influenza, and in management and conservation of wetlands. The study of evolution of genetic diversity and subsequent partitioning thereof during the last glaciation adds to ongoing discussions on the general evolution of waterfowl populations and flyway evolution. Hypothesised mallard flyways are tested explicitly by analysing mitochondrial mallard DNA from the whole northern hemisphere. Results Phylogenetic analyses confirm two mitochondrial mallard clades. Genetic differentiation within Eurasia and North-America is low, on a continental scale, but large differences occur between these two land masses (FST = 0.51). Half the genetic variance lies within sampling locations, and a negligible portion between currently recognised waterfowl flyways, within Eurasia and North-America. Analysis of molecular variance (AMOVA) at continent scale, incorporating sampling localities as smallest units, also shows the absence of population structure on the flyway level. Finally, demographic modelling by coalescence simulation proposes a split between Eurasia and North-America 43,000 to 74,000 years ago and strong population growth (~100fold) since then and little migration (not statistically different from zero). Conclusions Based on this first complete assessment of the mallard's world-wide population genetic structure we confirm that no more than two mtDNA clades exist. Clade A is characteristic for Eurasia, and clade B for North-America although some representatives of clade A are also found in North-America. We explain this pattern by evaluating competing hypotheses and conclude that a complex mix of historical, recent and anthropogenic factors shaped the current mallard populations. We refute population classification based on flyways proposed by ornithologists and managers, because they seem to have little biological meaning. Our results have implications for wetland management and conservation, with special regard to the release of farmed mallards for hunting, as well as for the possible transmission of Avian Influenza by mallards due to migration. PMID:22093799
NASA Astrophysics Data System (ADS)
Iepure, S.; Namiotko, T.; Montanari, A.; Brugiapaglia, E.; Mainiero, M.; Mariani, S.; Fiebig, M.
2012-04-01
Cave water and sediments from an extensive sulfidic, chemioautotrophic subterranean ecosystem in the hypogenic karst complex of Frasassi (northeastern Apennines of Italy) was analysed for modern and fossil ostracode assemblages. 22 extant and 16 extinct ostracode species make of this continental sulphidic ecosystem one of the richest worldwide. Both modern and fossil assemblages show the expected pattern of species diversity after the simulation procedure for taxonomic distinctness, which indicates no major extinction events since the Pleistocene. Extant species display patchy distribution according to habitat heterogeneity within the sulphidic environment. Fossil assemblages from a 3 m thick fluvial deposit trapped near the entrance of the Caverna del Carbone (CDC) at about 30 m above present river level, and a fine sand deposit resting at about the same elevation in Sala Duecento (SDS) within the Grotta Grande del Vento preliminarily dated with OSL at 111±17 ka are being investigated. The former deposit has yet to be dated but it represents probably a normal stratigraphic succession spanning a few tens of kyr, which was deposited when the cave entrance was at the reach of fluvial flooding, potentially recording the transition from the last interglacial Riss-Würm to the glacial Würm. Sediment samples from the SDS site yielded an ostracode assemblage represented by 12 species with a d18O signature of -5‰ and a well-diversified palinoflora assemblage indicating a transitional condition between steppe and temperate forest. The top sediment from the CDC site is characterized by a less diversified ostracode assemblage represented by 8 species, d18O of -3‰, and a poorly diversified palinoflora dominated by herbaceous plants and lesser pines, indicating a colder environment in the early stage of the last glacial. Additional information on the geometric morphometry approach of B-splines method applied to extant and fossil specimens of the hypogean Mixtacandona ostracode was used to identify microevolutionary patterns and environmentally cued variation. Analyses indicate the presence of one morphotype of a new species A of the group Mixtacandona riongessa, and three distinctive morphotypes of a species B of the group M. laisi-chappuisi occurring in stratigraphically distinct fluvial-cave sediments. Apparent difference in the disparity level between these species could be associated with their survival in different environmental conditions. Species A is found nowadays living exclusively in sulphidic cave waters, and was present in the system since at least the end of the last interglacial. The extraordinary high taxonomic and morphological diversity of ostracods reflects in situ evolutionary processes that have occurred under the cumulative effect of high environmental energy availability of subterranean sulphidic ecosystems, heterogeneous environmental conditions, and spatial and temporal isolation.
NASA Astrophysics Data System (ADS)
Kuria, Z. N.; Woldai, T.; van der Meer, F. D.; Barongo, J. O.
2010-06-01
Southern Kenya Rift has been known as a region of high geodynamic activity expressed by recent volcanism, geothermal activity and high rate of seismicity. The active faults that host these activities have not been investigated to determine their subsurface geometry, faulting intensity and constituents (fluids, sediments) for proper characterization of tectonic rift extension. Two different models of extension direction (E-W to ESE-WNW and NW-SE) have been proposed. However, they were based on limited field data and lacked subsurface investigations. In this research, we delineated active fault zones from ASTER image draped on ASTER DEM, together with relocated earthquakes. Subsequently, we combined field geologic mapping, electrical resistivity, ground magnetic traverses and aeromagnetic data to investigate the subsurface character of the active faults. Our results from structural studies identified four fault sets of different age and deformational styles, namely: normal N-S; dextral NW-SE; strike slip ENE-WSW; and sinistral NE-SW. The previous studies did not recognize the existence of the sinistral oblique slip NE-SW trending faults which were created under an E-W extension to counterbalance the NW-SE faults. The E-W extension has also been confirmed from focal mechanism solutions of the swarm earthquakes, which are located where all the four fault sets intersect. Our findings therefore, bridge the existing gap in opinion on neo-tectonic extension of the rift suggested by the earlier authors. Our results from resistivity survey show that the southern faults are in filled with fluid (0.05 and 0.2 Ωm), whereas fault zones to the north contain high resistivity (55-75 Ωm) material. The ground magnetic survey results have revealed faulting activity within active fault zones that do not contain fluids. In addition, the 2D inversion of the four aero-magnetic profiles (209 km long) revealed: major vertical to sub vertical faults (dipping 75-85° east or west); an uplifted, heavily fractured and deformed basin to the north (highly disturbed magnetic signatures) characteristic of on going active rifting; and a refined architecture of the asymmetry graben to the south with an intrarift horst, whose western graben is 4 km deep and eastern graben is much deeper (9 km), with a zone of significant break in magnetic signatures at that depth, interpreted as source of the hot springs south of Lake Magadi (a location confirmed near surface by ground magnetic and resistivity data sets). The magnetic sources to the north are shallow at 15 km depth compared to 22 km to the south. The loss of magnetism to the north is probably due to increased heat as a result of magmatic intrusion supporting active rifting model. Conclusively, the integrated approach employed in this research confirms that fault system delineated to the north is actively deforming under E-W normal extension and is a potential earthquake source probably related to magmatic intrusion, while the presence of fluids within the south fault zone reduce intensity of faulting activity and explains lack of earthquakes in a continental rift setting.
Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang
2014-01-01
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109
Prediction on carbon dioxide emissions based on fuzzy rules
NASA Astrophysics Data System (ADS)
Pauzi, Herrini; Abdullah, Lazim
2014-06-01
There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.
On the criticality of inferred models
NASA Astrophysics Data System (ADS)
Mastromatteo, Iacopo; Marsili, Matteo
2011-10-01
Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.
KENNETH M. HANSON; JANE M. BOOKER
2000-09-08
The authors an uncertainty analysis of data taken using the Rossi technique, in which the horizontal oscilloscope sweep is driven sinusoidally in time ,while the vertical axis follows the signal amplitude. The analysis is done within a Bayesian framework. Complete inferences are obtained by tilting the Markov chain Monte Carlo technique, which produces random samples from the posterior probability distribution expressed in terms of the parameters.
NASA Astrophysics Data System (ADS)
Kadkhodaie-Ilkhchi, Ali; Rahimpour-Bonab, Hossain; Rezaee, Mohammadreza
2009-03-01
Total organic carbon (TOC) content present in reservoir rocks is one of the important parameters, which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon-bearing units. In general, organic-rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher γ-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into 87 training sets to build the CMIS model and 37 testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.
NASA Astrophysics Data System (ADS)
von Toussaint, Udo
2011-07-01
Bayesian inference provides a consistent method for the extraction of information from physics experiments even in ill-conditioned circumstances. The approach provides a unified rationale for data analysis, which both justifies many of the commonly used analysis procedures and reveals some of the implicit underlying assumptions. This review summarizes the general ideas of the Bayesian probability theory with emphasis on the application to the evaluation of experimental data. As case studies for Bayesian parameter estimation techniques examples ranging from extra-solar planet detection to the deconvolution of the apparatus functions for improving the energy resolution and change point estimation in time series are discussed. Special attention is paid to the numerical techniques suited for Bayesian analysis, with a focus on recent developments of Markov chain Monte Carlo algorithms for high-dimensional integration problems. Bayesian model comparison, the quantitative ranking of models for the explanation of a given data set, is illustrated with examples collected from cosmology, mass spectroscopy, and surface physics, covering problems such as background subtraction and automated outlier detection. Additionally the Bayesian inference techniques for the design and optimization of future experiments are introduced. Experiments, instead of being merely passive recording devices, can now be designed to adapt to measured data and to change the measurement strategy on the fly to maximize the information of an experiment. The applied key concepts and necessary numerical tools which provide the means of designing such inference chains and the crucial aspects of data fusion are summarized and some of the expected implications are highlighted.
Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L
2013-02-01
The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies. PMID:23266912
Decision generation tools and Bayesian inference
NASA Astrophysics Data System (ADS)
Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas
2014-05-01
Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.
Distributed generation system using wind/photovoltaic/fuel cell
NASA Astrophysics Data System (ADS)
Buasri, Panhathai
This dissertation investigates the performance and the operation of a distributed generation (DG) power system using wind/photovoltaic/fuel cell (W/PV/FC). The power system consists of a 2500 W photovoltaic array subsystem, a 500 W proton exchange membrane fuel cell (PEMFC) stack subsystem, 300 W wind turbine, 500 W wind turbine, and 1500 W wind energy conversion subsystems. To extract maximum power from the PV, a maximum power point tracker was designed and fabricated. A 4 kW single phase inverter was used to convert the DC voltage to AC voltage; also a 44 kWh battery bank was used to store energy and prevent fluctuation of the power output of the DG system. To connect the fuel cell to the batteries, a DC/DC controller was designed and fabricated. To monitor and study the performance of the DG system under variable conditions, a data acquisition system was designed and installed. The fuel cell subsystem performance was evaluated under standalone operation using a variable resistance and under interactive mode, connected to the batteries. The manufacturing data and the experimental data were used to develop an electrical circuit model to the fuel cell. Furthermore, harmonic analysis of the DG system was investigated. For an inverter, the AC voltage delivered to the grid changed depending on the time, load, and electronic equipment that was connected. The quality of the DG system was evaluated by investigating the harmonics generated by the power electronics converters. Finally, each individual subsystem of the DG system was modeled using the neuro-fuzzy approach. The model was used to predict the performance of the DG system under variable conditions, such as passing clouds and wind gust conditions. The steady-state behaviors of the model were validated by the experimental results under different operating conditions.
NASA Astrophysics Data System (ADS)
Al-Abadi, Alaa M.
2014-12-01
The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi-Sugeno (TS) inference system for mimic stage-discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models' results were compared using root mean squared error (RMSE) and coefficient of determination (R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi-Sugeno techniques have certain advantages for setting up stage-discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage-discharge relationship in the study area.
ERIC Educational Resources Information Center
Briggs, Derek C.
2010-01-01
The use of large-scale assessments for making high stakes inferences about students and the schools in which they are situated is premised on the assumption that tests are sensitive to good instruction. An increase in the quality of classroom instruction should cause, on the average, an increase in test scores. In work with a number of colleagues…
NASA Technical Reports Server (NTRS)
Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter
2002-01-01
The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.
Infering Networks From Collective Dynamics
NASA Astrophysics Data System (ADS)
Timme, Marc
How can we infer direct physical interactions between pairs of units from only knowing the units' time series? Here we present a dynamical systems' view on collective network dynamics, and propose the concept of a dynamics' space to reveal interaction networks from time series. We present two examples: one, where the time series stem from standard ordinary differential equations, and a second, more abstract, where the time series exhibits only partial information about the units' states. We apply the latter to neural circuit dynamics where the observables are spike timing data, i.e. only a discrete, state-dependent outputs of the neurons. These results may help revealing network structure for systems where direct access to dynamics is simpler than to connectivity, cf.. This is work with Jose Casadiego, Srinivas Gorur Shandilya, Mor Nitzan, Hauke Haehne and Dimitra Maoutsa. Supported by Grants of the BMBF (Future Compliant Power Grids - CoNDyNet) and by the Max Planck Society to MT.
Quantum Inference on Bayesian Networks
NASA Astrophysics Data System (ADS)
Yoder, Theodore; Low, Guang Hao; Chuang, Isaac
2014-03-01
Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.
SYMBOLIC INFERENCE OF XENOBIOTIC METABOLISM
MCSHAN, D.C.; UPDADHAYAYA, M.; SHAH, I.
2009-01-01
We present a new symbolic computational approach to elucidate the biochemical networks of living systems de novo and we apply it to an important biomedical problem: xenobiotic metabolism. A crucial issue in analyzing and modeling a living organism is understanding its biochemical network beyond what is already known. Our objective is to use the available metabolic information in a representational framework that enables the inference of novel biochemical knowledge and whose results can be validated experimentally. We describe a symbolic computational approach consisting of two parts. First, biotransformation rules are inferred from the molecular graphs of compounds in enzyme-catalyzed reactions. Second, these rules are recursively applied to different compounds to generate novel metabolic networks, containing new biotransformations and new metabolites. Using data for 456 generic reactions and 825 generic compounds from KEGG we were able to extract 110 biotransformation rules, which generalize a subset of known biocatalytic functions. We tested our approach by applying these rules to ethanol, a common substance of abuse and to furfuryl alcohol, a xenobiotic organic solvent, which is absent in metabolic databases. In both cases our predictions on the fate of ethanol and furfuryl alcohol are consistent with the literature on the metabolism of these compounds. PMID:14992532
Inferring Horizontal Gene Transfer
Lassalle, Florent; Dessimoz, Christophe
2015-01-01
Horizontal or Lateral Gene Transfer (HGT or LGT) is the transmission of portions of genomic DNA between organisms through a process decoupled from vertical inheritance. In the presence of HGT events, different fragments of the genome are the result of different evolutionary histories. This can therefore complicate the investigations of evolutionary relatedness of lineages and species. Also, as HGT can bring into genomes radically different genotypes from distant lineages, or even new genes bearing new functions, it is a major source of phenotypic innovation and a mechanism of niche adaptation. For example, of particular relevance to human health is the lateral transfer of antibiotic resistance and pathogenicity determinants, leading to the emergence of pathogenic lineages [1]. Computational identification of HGT events relies upon the investigation of sequence composition or evolutionary history of genes. Sequence composition-based ("parametric") methods search for deviations from the genomic average, whereas evolutionary history-based ("phylogenetic") approaches identify genes whose evolutionary history significantly differs from that of the host species. The evaluation and benchmarking of HGT inference methods typically rely upon simulated genomes, for which the true history is known. On real data, different methods tend to infer different HGT events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events. PMID:26020646
Circular inferences in schizophrenia.
Jardri, Renaud; Denève, Sophie
2013-11-01
A considerable number of recent experimental and computational studies suggest that subtle impairments of excitatory to inhibitory balance or regulation are involved in many neurological and psychiatric conditions. The current paper aims to relate, specifically and quantitatively, excitatory to inhibitory imbalance with psychotic symptoms in schizophrenia. Considering that the brain constructs hierarchical causal models of the external world, we show that the failure to maintain the excitatory to inhibitory balance results in hallucinations as well as in the formation and subsequent consolidation of delusional beliefs. Indeed, the consequence of excitatory to inhibitory imbalance in a hierarchical neural network is equated to a pathological form of causal inference called 'circular belief propagation'. In circular belief propagation, bottom-up sensory information and top-down predictions are reverberated, i.e. prior beliefs are misinterpreted as sensory observations and vice versa. As a result, these predictions are counted multiple times. Circular inference explains the emergence of erroneous percepts, the patient's overconfidence when facing probabilistic choices, the learning of 'unshakable' causal relationships between unrelated events and a paradoxical immunity to perceptual illusions, which are all known to be associated with schizophrenia. PMID:24065721
Continuity of the Maximum-Entropy Inference
NASA Astrophysics Data System (ADS)
Stephan, Weis
2014-09-01
We study the inverse problem of inferring the state of a finite-level quantum system from expected values of a fixed set of observables, by maximizing a continuous ranking function. We have proved earlier that the maximum-entropy inference can be a discontinuous map from the convex set of expected values to the convex set of states because the image contains states of reduced support, while this map restricts to a smooth parametrization of a Gibbsian family of fully supported states. Here we prove for arbitrary ranking functions that the inference is continuous up to boundary points. This follows from a continuity condition in terms of the openness of the restricted linear map from states to their expected values. The openness condition shows also that ranking functions with a discontinuous inference are typical. Moreover it shows that the inference is continuous in the restriction to any polytope which implies that a discontinuity belongs to the quantum domain of non-commutative observables and that a geodesic closure of a Gibbsian family equals the set of maximum-entropy states. We discuss eight descriptions of the set of maximum-entropy states with proofs of accuracy and an analysis of deviations.
Automation of Large-scale Computer Cluster Monitoring Information Analysis
NASA Astrophysics Data System (ADS)
Magradze, Erekle; Nadal, Jordi; Quadt, Arnulf; Kawamura, Gen; Musheghyan, Haykuhi
2015-12-01
High-throughput computing platforms consist of a complex infrastructure and provide a number of services apt to failures. To mitigate the impact of failures on the quality of the provided services, a constant monitoring and in time reaction is required, which is impossible without automation of the system administration processes. This paper introduces a way of automation of the process of monitoring information analysis to provide the long and short term predictions of the service response time (SRT) for a mass storage and batch systems and to identify the status of a service at a given time. The approach for the SRT predictions is based on Adaptive Neuro Fuzzy Inference System (ANFIS). An evaluation of the approaches is performed on real monitoring data from the WLCG Tier 2 center GoeGrid. Ten fold cross validation results demonstrate high efficiency of both approaches in comparison to known methods.
BIE: Bayesian Inference Engine
NASA Astrophysics Data System (ADS)
Weinberg, Martin D.
2013-12-01
The Bayesian Inference Engine (BIE) is an object-oriented library of tools written in C++ designed explicitly to enable Bayesian update and model comparison for astronomical problems. To facilitate "what if" exploration, BIE provides a command line interface (written with Bison and Flex) to run input scripts. The output of the code is a simulation of the Bayesian posterior distribution from which summary statistics e.g. by taking moments, or determine confidence intervals and so forth, can be determined. All of these quantities are fundamentally integrals and the Markov Chain approach produces variates heta distributed according to P( heta|D) so moments are trivially obtained by summing of the ensemble of variates.
Structural inference for uncertain networks.
Martin, Travis; Ball, Brian; Newman, M E J
2016-01-01
In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding. We also give an example application to the detection of communities in a protein-protein interaction network. PMID:26871091
Structural inference for uncertain networks
NASA Astrophysics Data System (ADS)
Martin, Travis; Ball, Brian; Newman, M. E. J.
2016-01-01
In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding. We also give an example application to the detection of communities in a protein-protein interaction network.
Bayes factors and multimodel inference
Link, W.A.; Barker, R.J.
2009-01-01
Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.
Inferring Characters' Emotional States: Can Readers Infer Specific Emotions?
ERIC Educational Resources Information Center
Gygax, Pascal; Garnham, Alan; Oakhill, Jane
2004-01-01
Gygax, Oakhill and Garnham (2003) showed that, contrary to the assumption of earlier research, readers do not infer specific emotions such as "guilt" or "boredom". This paper presents evidence for the non-specificity of emotional inferences regardless of the nature of the stories. In Experiment 1 and 2, Gygax et al.'s stories were made longer. In…
ANFIS based modeling and inverse control of a thin SMA wire
NASA Astrophysics Data System (ADS)
Kilicarslan, Atilla; Song, Gangbing; Grigoriadis, Karolos
2008-03-01
In this work, we propose an Adaptive Neuro Fuzzy Inference System (ANFIS) based hysteresis modeling and control strategy for a thin Shape Memory Alloy (SMA) wire. Controlling the SMA wire is a challenging problem because of its dynamic hysteretic behavior. By using a hybrid learning procedure ANFIS architectures are powerful tools for many applications, such as identifying nonlinear parameters in a controlled system, predicting chaotic time series and modeling nonlinear functions. We tested our ANFIS model by making it predict major and minor hysteresis loops in different driving frequencies and compared them with the experimental data. To compensate the hysteretic effect, we used an inverse ANFIS model and used it directly as a controller. After dramatically reducing the hysteretic effect, we implemented a PI control to fine tune the response.
Anfis Approach for Sssc Controller Design for the Improvement of Transient Stability Performance
NASA Astrophysics Data System (ADS)
Khuntia, Swasti R.; Panda, Sidhartha
2011-06-01
In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Static Synchronous Series Compensator (SSSC)-based controller for improvement of transient stability. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controller of SSSC. It is observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances. The results prove that the proposed SSSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for SSSC.
Causal network inference using biochemical kinetics
Oates, Chris J.; Dondelinger, Frank; Bayani, Nora; Korkola, James; Gray, Joe W.; Mukherjee, Sach
2014-01-01
Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems. Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown. Availability and implementation: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates. Contact: c.oates@warwick.ac.uk or sach@mrc-bsu.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161235
Causal Inference in Retrospective Studies.
ERIC Educational Resources Information Center
Holland, Paul W.; Rubin, Donald B.
1988-01-01
The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…
Causal Inference and Developmental Psychology
ERIC Educational Resources Information Center
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
Improving Inferences from Multiple Methods.
ERIC Educational Resources Information Center
Shotland, R. Lance; Mark, Melvin M.
1987-01-01
Multiple evaluation methods (MEMs) can cause an inferential challenge, although there are strategies to strengthen inferences. Practical and theoretical issues involved in the use by social scientists of MEMs, three potential problems in drawing inferences from MEMs, and short- and long-term strategies for alleviating these problems are outlined.
Improving Inferences from Multiple Methods.
ERIC Educational Resources Information Center
Shotland, R. Lance; Mark, Melvin M.
1987-01-01
Multiple evaluation methods (MEMs) can cause an inferential challenge, although there are strategies to strengthen inferences. Practical and theoretical issues involved in the use by social scientists of MEMs, three potential problems in drawing inferences from MEMs, and short- and long-term strategies for alleviating these problems are outlined.…
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
NASA Astrophysics Data System (ADS)
Shiri, Jalal; Ki?i, zgur; Landeras, Gorka; Lpez, Jos Javier; Nazemi, Amir Hossein; Stuyt, Louis C. P. M.
2012-01-01
SummaryEvapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. This study presents a Gene Expression Programming (GEP) approach, for estimating daily reference evapotranspiration ( ET0) in four weather stations in Basque Country (Northern Spain), for a 5-year period (1999-2003). The data set comprising air temperature, relative humidity, wind speed and solar radiation was employed for modeling ET0 using FAO-56 Penman Monteith equation as the reference. The GEP results were compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS), Priestley-Taylor and Hargreaves-Samani models. Based on the comparisons, the GEP was found to perform better than the ANFIS, Priestley-Taylor and Hargreaves-Samani models. The ANFIS model is ranked as the second best model.
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Shiri, Jalal
2012-06-01
Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg-Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others.
NASA Astrophysics Data System (ADS)
Shamsipour, Majid; Pahlevani, Zahra; Shabani, Mohsen Ostad; Mazahery, Ali
2016-04-01
Understanding of the electromagnetic stirrer (EMS) process parameters-wear relation in nanocomposite is required for further creation of tailored modifications of process in accordance with the demands for various applications. This study depicts the performance of hybrid algorithm for optimization of the parameters in EMS compocasting of nano-TiC-reinforced Al-Si alloys. Adaptive neuro-fuzzy inference system (ANFIS) coupled with particle swarm optimization (PSO) was applied to find the optimum combination of the inputs including mold temperature, mix time, impeller speed, powder temperature, cast temperature and average particle size. The optimized condition was obtained in minimization of objective function. The objective function is calculated by ANFIS and then minimized by PSO. The optimized parameters were used to produce semisolid cast aluminum matrix composites reinforced with nano-TiC particles. The optimized nanocomposites were then studied for their tribological properties.
3D image analysis and artificial intelligence for bone disease classification.
Akgundogdu, Abdurrahim; Jennane, Rachid; Aufort, Gabriel; Benhamou, Claude Laurent; Ucan, Osman Nuri
2010-10-01
In order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm. PMID:20703627
Abbaspour, Sara; Fallah, Ali; Lindén, Maria; Gholamhosseini, Hamid
2016-02-01
In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97dB and 0.02 respectively and a significantly higher correlation coefficient (p<0.05). PMID:26643795
Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.
2011-01-01
The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.
NASA Astrophysics Data System (ADS)
Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal
2014-06-01
This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.
Forecasting daily lake levels using artificial intelligence approaches
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher
2012-04-01
Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.
Soft computing methods for geoidal height transformation
NASA Astrophysics Data System (ADS)
Akyilmaz, O.; Özlüdemir, M. T.; Ayan, T.; Çelik, R. N.
2009-07-01
Soft computing techniques, such as fuzzy logic and artificial neural network (ANN) approaches, have enabled researchers to create precise models for use in many scientific and engineering applications. Applications that can be employed in geodetic studies include the estimation of earth rotation parameters and the determination of mean sea level changes. Another important field of geodesy in which these computing techniques can be applied is geoidal height transformation. We report here our use of a conventional polynomial model, the Adaptive Network-based Fuzzy (or in some publications, Adaptive Neuro-Fuzzy) Inference System (ANFIS), an ANN and a modified ANN approach to approximate geoid heights. These approximation models have been tested on a number of test points. The results obtained through the transformation processes from ellipsoidal heights into local levelling heights have also been compared.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-01-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468
Autonomous agricultural remote sensing systems with high spatial and temporal resolutions
NASA Astrophysics Data System (ADS)
Xiang, Haitao
In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield consistently for both seasons. The VT-R4 stages are the best period of time to estimate the corn yield. The SICMS system is only suitable for the RS research at a fixed location. In order to provide more flexibility of the RS image collection, a novel UAV based system has been studied. The UAV based agricultural RS system used a light helicopter platform equipped with a multi-spectral camera. The UAV control system consisted of an on-board and a ground station subsystem. For the on-board subsystem, an Extended Kalman Filter (EKF) based UAV navigation system was designed and implemented. The navigation system, using low cost inertial sensors, magnetometer, GPS and a single board computer, was capable of providing continuous estimates of UAV position and attitude at 50 Hz using sensor fusion techniques. The ground station subsystem was designed to be an interface between a human operator and the UAV to implement mission planning, flight command activation, and real-time flight monitoring. The navigation system is controlled by the ground station, and able to navigate the UAV in the air to reach the predefined waypoints and trigger the multi-spectral camera. By so doing, the aerial images at each point could be captured automatically. The developed UAV RS system can provide a maximum flexibility in crop field RS image collection. It is essential to perform the geometric correction and the geocoding before an aerial image can be used for precision farming. An automatic (no Ground Control Point (GCP) needed) UAV image georeferencing algorithm was developed. This algorithm can do the automatic image correction and georeferencing based on the real-time navigation data and a camera lens distortion model. The accuracy of the georeferencing algorithm was better than 90 cm according to a series test. The accuracy that has been achieved indicates that, not only is the position solution good, but the attitude error is extremely small. The waypoints planning for UAV flight was investigated. It suggested that a 16.5% forward overlap and a 15% lateral overlap were required to avoiding missing desired mapping area when the UAV flies above 45 m high with 4.5 mm lens. A whole field mosaic image can be generated according to the individual image georeferencing information. A 0.569 m mosaic error has been achieved and this accuracy is sufficient for many of the intended precision agricultural applications. With careful interpretation, the UAV images are an excellent source of high spatial and temporal resolution data for precision agricultural applications. (Abstract shortened by UMI.)
Double jeopardy in inferring cognitive processes
Fific, Mario
2014-01-01
Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2n. In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545
Structure inference for Bayesian multisensory scene understanding.
Hospedales, Timothy M; Vijayakumar, Sethu
2008-12-01
We investigate a solution to the problem of multi-sensor scene understanding by formulating it in the framework of Bayesian model selection and structure inference. Humans robustly associate multimodal data as appropriate, but previous modelling work has focused largely on optimal fusion, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, Bayesian solution to multi-sensor perception and tracking which accounts for both integration and segregation by explicit probabilistic reasoning about data association in a temporal context. Such explicit inference of multimodal data association is also of intrinsic interest for higher level understanding of multisensory data. We illustrate this using a probabilistic implementation of data association in a multi-party audio-visual scenario, where unsupervised learning and structure inference is used to automatically segment, associate and track individual subjects in audiovisual sequences. Indeed, the structure inference based framework introduced in this work provides the theoretical foundation needed to satisfactorily explain many confounding results in human psychophysics experiments involving multimodal cue integration and association. PMID:18988948
Efficient Bayesian inference for ARFIMA processes
NASA Astrophysics Data System (ADS)
Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.
2015-03-01
Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.
Inverse Ising inference with correlated samples
NASA Astrophysics Data System (ADS)
Obermayer, Benedikt; Levine, Erel
2014-12-01
Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem.
Ensemble Inference and Inferability of Gene Regulatory Networks
Ud-Dean, S. M. Minhaz; Gunawan, Rudiyanto
2014-01-01
The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge. PMID:25093509
The empirical accuracy of uncertain inference models
NASA Technical Reports Server (NTRS)
Vaughan, David S.; Yadrick, Robert M.; Perrin, Bruce M.; Wise, Ben P.
1987-01-01
Uncertainty is a pervasive feature of the domains in which expert systems are designed to function. Research design to test uncertain inference methods for accuracy and robustness, in accordance with standard engineering practice is reviewed. Several studies were conducted to assess how well various methods perform on problems constructed so that correct answers are known, and to find out what underlying features of a problem cause strong or weak performance. For each method studied, situations were identified in which performance deteriorates dramatically. Over a broad range of problems, some well known methods do only about as well as a simple linear regression model, and often much worse than a simple independence probability model. The results indicate that some commercially available expert system shells should be used with caution, because the uncertain inference models that they implement can yield rather inaccurate results.
Biological Network Inference from Microarray Data, Current Solutions, and Assessments.
Roy, Swarup; Guzzi, Pietro Hiram
2016-01-01
Currently in bioinformatics and systems biology there is a growing interest for the analysis of associations among biological molecules at a network level. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. The current scenario is characterized by a growing number of algorithms for the inference, while few attention has been posed on the determination of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard datasets. Here we survey some selected inference algorithms and we compare current assessments. We also present a systematic listing of freely available inference and assessment tools for easy reference. Finally we outline some possible future directions of research, such as the use of a prior knowledge into the assessment process. PMID:26507508
Quantum-Like Representation of Non-Bayesian Inference
NASA Astrophysics Data System (ADS)
Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.
2013-01-01
This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.
Abductive inference and delusional belief.
Coltheart, Max; Menzies, Peter; Sutton, John
2010-01-01
Delusional beliefs have sometimes been considered as rational inferences from abnormal experiences. We explore this idea in more detail, making the following points. First, the abnormalities of cognition that initially prompt the entertaining of a delusional belief are not always conscious and since we prefer to restrict the term "experience" to consciousness we refer to "abnormal data" rather than "abnormal experience". Second, we argue that in relation to many delusions (we consider seven) one can clearly identify what the abnormal cognitive data are which prompted the delusion and what the neuropsychological impairment is which is responsible for the occurrence of these data; but one can equally clearly point to cases where this impairment is present but delusion is not. So the impairment is not sufficient for delusion to occur: a second cognitive impairment, one that affects the ability to evaluate beliefs, must also be present. Third (and this is the main thrust of our paper), we consider in detail what the nature of the inference is that leads from the abnormal data to the belief. This is not deductive inference and it is not inference by enumerative induction; it is abductive inference. We offer a Bayesian account of abductive inference and apply it to the explanation of delusional belief. PMID:20017038
Active inference, communication and hermeneutics☆
Friston, Karl J.; Frith, Christopher D.
2015-01-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007
Active inference, communication and hermeneutics.
Friston, Karl J; Frith, Christopher D
2015-07-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007
Optimal inference with suboptimal models: addiction and active Bayesian inference.
Schwartenbeck, Philipp; FitzGerald, Thomas H B; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl
2015-02-01
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent's beliefs - based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment - as opposed to the agent's beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less 'optimally' than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject's generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described 'limited offer' task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321
Optimal inference with suboptimal models: Addiction and active Bayesian inference
Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl
2015-01-01
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321
Maximum caliber inference of nonequilibrium processes.
Otten, Moritz; Stock, Gerhard
2010-07-21
Thirty years ago, Jaynes suggested a general theoretical approach to nonequilibrium statistical mechanics, called maximum caliber (MaxCal) [Annu. Rev. Phys. Chem. 31, 579 (1980)]. MaxCal is a variational principle for dynamics in the same spirit that maximum entropy is a variational principle for equilibrium statistical mechanics. Motivated by the success of maximum entropy inference methods for equilibrium problems, in this work the MaxCal formulation is applied to the inference of nonequilibrium processes. That is, given some time-dependent observables of a dynamical process, one constructs a model that reproduces these input data and moreover, predicts the underlying dynamics of the system. For example, the observables could be some time-resolved measurements of the folding of a protein, which are described by a few-state model of the free energy landscape of the system. MaxCal then calculates the probabilities of an ensemble of trajectories such that on average the data are reproduced. From this probability distribution, any dynamical quantity of the system can be calculated, including population probabilities, fluxes, or waiting time distributions. After briefly reviewing the formalism, the practical numerical implementation of MaxCal in the case of an inference problem is discussed. Adopting various few-state models of increasing complexity, it is demonstrated that the MaxCal principle indeed works as a practical method of inference: The scheme is fairly robust and yields correct results as long as the input data are sufficient. As the method is unbiased and general, it can deal with any kind of time dependency such as oscillatory transients and multitime decays. PMID:20649320
Statistical inference and string theory
NASA Astrophysics Data System (ADS)
Heckman, Jonathan J.
2015-09-01
In this paper, we expose some surprising connections between string theory and statistical inference. We consider a large collective of agents sweeping out a family of nearby statistical models for an M-dimensional manifold of statistical fitting parameters. When the agents making nearby inferences align along a d-dimensional grid, we find that the pooled probability that the collective reaches a correct inference is the partition function of a nonlinear sigma model in d dimensions. Stability under perturbations to the original inference scheme requires the agents of the collective to distribute along two dimensions. Conformal invariance of the sigma model corresponds to the condition of a stable inference scheme, directly leading to the Einstein field equations for classical gravity. By summing over all possible arrangements of the agents in the collective, we reach a string theory. We also use this perspective to quantify how much an observer can hope to learn about the internal geometry of a superstring compactification. Finally, we present some brief speculative remarks on applications to the AdS/CFT correspondence and Lorentzian signature space-times.
Strategic Production of Predictive Inferences During Comprehension
ERIC Educational Resources Information Center
Allbritton, David
2004-01-01
Although some types of inferences are mandatory for readers, predictive inferences (inferences for what will happen next) are generally considered elaborative or optional. Three experiments measuring probe word lexical decision latencies produced evidence for the online generation of predictive inferences during narrative text comprehension.…
CAUSAL INFERENCE IN BIOLOGY NETWORKS WITH INTEGRATED BELIEF PROPAGATION
CHANG, RUI; KARR, JONATHAN R; SCHADT, ERIC E
2014-01-01
Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the ‘fitness’ of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot. PMID:25592596
Inferring Epidemic Network Topology from Surveillance Data
Wan, Xiang; Liu, Jiming; Cheung, William K.; Tong, Tiejun
2014-01-01
The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases. PMID:24979215
NASA Astrophysics Data System (ADS)
Golsanami, Naser; Kadkhodaie-Ilkhchi, Ali; Erfani, Amir
2015-01-01
Capillary pressure curves are important data for reservoir rock typing, analyzing pore throat distribution, determining height above free water level, and reservoir simulation. Laboratory experiments provide accurate data, however they are expensive, time-consuming and discontinuous through the reservoir intervals. The current study focuses on synthesizing artificial capillary pressure (Pc) curves from seismic attributes with the use of artificial intelligent systems including Artificial Neural Networks (ANNs), Fuzzy logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs). The synthetic capillary pressure curves were achieved by estimating pressure values at six mercury saturation points. These points correspond to mercury filled pore volumes of core samples (Hg-saturation) at 5%, 20%, 35%, 65%, 80%, and 90% saturations. To predict the synthetic Pc curve at each saturation point, various FL, ANFIS and ANN models were constructed. The varying neural network models differ in their training algorithm. Based on the performance function, the most accurately functioning models were selected as the final solvers to do the prediction process at each of the above-mentioned mercury saturation points. The constructed models were then tested at six depth points of the studied well which were already unforeseen by the models. The results show that the Fuzzy logic and neuro-fuzzy models were not capable of making reliable estimations, while the predictions from the ANN models were satisfyingly trustworthy. The obtained results showed a good agreement between the laboratory derived and synthetic capillary pressure curves. Finally, a 3D seismic cube was captured for which the required attributes were extracted and the capillary pressure cube was estimated by using the developed models. In the next step, the synthesized Pc cube was compared with the seismic cube and an acceptable correspondence was observed.
Inferring Trust Based on Similarity with TILLIT
NASA Astrophysics Data System (ADS)
Tavakolifard, Mozhgan; Herrmann, Peter; Knapskog, Svein J.
A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of today’s most successful e-commerce and recommendation systems. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Trust inferences are transitive associations among users in the context of an underlying social network and may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. An alternative approach to the problem is advocated in this paper. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different trusters, one can assume that they are similar. Thus, if one has a certain degree of trust to the one party, one can safely assume a very similar trustworthiness of the other one. In an attempt to provide high quality recommendations and proper initial trust values even when no complete trust propagation path or user profile exists, we propose TILLIT — a model based on combination of trust inferences and user similarity. The similarity is derived from the structure of the trust graph and users’ trust behavior as opposed to other collaborative-filtering based approaches which use ratings of items or user’s profile. We describe an algorithm realizing the approach based on a combination of trust inferences and user similarity, and validate the algorithm using a real large-scale data-set.
NASA Technical Reports Server (NTRS)
Halem, M.; Kalnay-Rivas, E.; Baker, W. E.; Atlas, R.
1981-01-01
The statistical properties, and coverage, of satellite temperature sounding data are described. Tropical regions are observed every two days, extratropics from one to four times a day. Oceans are covered two to three times a day. Asynoptic coverage is comparable to the U.S. rawinsonde network twice daily coverage. Lack of ground truth for data sparse areas makes accuracy difficult to assess. The rms differences of layer mean temperatures obtained from collocating rawinsonde observations with satellite temperature profiles in space and time differ from rms differences of layer mean satellite temperature soundings. The FGGE satellite systems can infer the three dimensional motion field and improve the representation of the large scale state of the atmosphere.
NASA Astrophysics Data System (ADS)
Christophoul, Frédéric; Baby, Patrice; Soula, Jean-Claude; Rosero, Michel; Burgos, José
2002-11-01
A sedimentological study of the Neogene continental infill of the Subandean foreland basin of Ecuador led us to define an evolution of the fluvial system from an alluvial plain to an alluvial fan with an increasing slope in the same time as the drainage changed from mostly longitudinal to transverse. Combined with the data presently available on palaeotopography, exhumation, tectonic evolution and geomorphology, these results enable us to infer that, in contrast with the other Subandean foreland basins of Bolivia and Peru, the progradation of the Neogene alluvial fans proceeded by an overall expansion, associated with a relatively small tectonic shortening and not as a result of the development of successive thrust-related depocentres. This also indicates that the surrection of the Cordillera progressed in Ecuador throughout the Neogene. To cite this article: F. Christophoul et al., C. R. Geoscience 334 (2002) 1029-1037.
Algorithm Optimally Orders Forward-Chaining Inference Rules
NASA Technical Reports Server (NTRS)
James, Mark
2008-01-01
People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.
How Forgetting Aids Heuristic Inference
ERIC Educational Resources Information Center
Schooler, Lael J.; Hertwig, Ralph
2005-01-01
Some theorists, ranging from W. James (1890) to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2…
Perceptual Inference and Autistic Traits
ERIC Educational Resources Information Center
Skewes, Joshua C; Jegindø, Else-Marie; Gebauer, Line
2015-01-01
Autistic people are better at perceiving details. Major theories explain this in terms of bottom-up sensory mechanisms or in terms of top-down cognitive biases. Recently, it has become possible to link these theories within a common framework. This framework assumes that perception is implicit neural inference, combining sensory evidence with…
The mechanisms of temporal inference
NASA Technical Reports Server (NTRS)
Fox, B. R.; Green, S. R.
1987-01-01
The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.
Sample Size and Correlational Inference
ERIC Educational Resources Information Center
Anderson, Richard B.; Doherty, Michael E.; Friedrich, Jeff C.
2008-01-01
In 4 studies, the authors examined the hypothesis that the structure of the informational environment makes small samples more informative than large ones for drawing inferences about population correlations. The specific purpose of the studies was to test predictions arising from the signal detection simulations of R. B. Anderson, M. E. Doherty,…
Improving Explanatory Inferences from Assessments
ERIC Educational Resources Information Center
Diakow, Ronli Phyllis
2013-01-01
This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…
Science Shorts: Observation versus Inference
ERIC Educational Resources Information Center
Leager, Craig R.
2008-01-01
When you observe something, how do you know for sure what you are seeing, feeling, smelling, or hearing? Asking students to think critically about their encounters with the natural world will help to strengthen their understanding and application of the science-process skills of observation and inference. In the following lesson, students make…
Starfish: Robust spectroscopic inference tools
NASA Astrophysics Data System (ADS)
Czekala, Ian; Andrews, Sean M.; Mandel, Kaisey S.; Hogg, David W.; Green, Gregory M.
2015-05-01
Starfish is a set of tools used for spectroscopic inference. It robustly determines stellar parameters using high resolution spectral models and uses Markov Chain Monte Carlo (MCMC) to explore the full posterior probability distribution of the stellar parameters. Additional potential applications include other types of spectra, such as unresolved stellar clusters or supernovae spectra.
Teaching Inference for Randomized Experiments
ERIC Educational Resources Information Center
Ernst, Michael D.
2009-01-01
Nearly all introductory statistics textbooks include a chapter on data collection methods that includes a detailed discussion of both random sampling methods and randomized experiments. But when statistical inference is introduced in subsequent chapters, its justification is nearly always based on principles of random sampling methods. From the
Marateb, Hamid Reza; Goudarzi, Sobhan
2015-01-01
Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were “age and ST/heart rate slope categories,” “exercise-induced angina status,” fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed “substantial agreement” with the gold standard. This algorithm is thus, a promising tool for screening CAD patients. PMID:26109965
Degradation monitoring using probabilistic inference
NASA Astrophysics Data System (ADS)
Alpay, Bulent
In order to increase safety and improve economy and performance in a nuclear power plant (NPP), the source and extent of component degradations should be identified before failures and breakdowns occur. It is also crucial for the next generation of NPPs, which are designed to have a long core life and high fuel burnup to have a degradation monitoring system in order to keep the reactor in a safe state, to meet the designed reactor core lifetime and to optimize the scheduled maintenance. Model-based methods are based on determining the inconsistencies between the actual and expected behavior of the plant, and use these inconsistencies for detection and diagnostics of degradations. By defining degradation as a random abrupt change from the nominal to a constant degraded state of a component, we employed nonlinear filtering techniques based on state/parameter estimation. We utilized a Bayesian recursive estimation formulation in the sequential probabilistic inference framework and constructed a hidden Markov model to represent a general physical system. By addressing the problem of a filter's inability to estimate an abrupt change, which is called the oblivious filter problem in nonlinear extensions of Kalman filtering, and the sample impoverishment problem in particle filtering, we developed techniques to modify filtering algorithms by utilizing additional data sources to improve the filter's response to this problem. We utilized a reliability degradation database that can be constructed from plant specific operational experience and test and maintenance reports to generate proposal densities for probable degradation modes. These are used in a multiple hypothesis testing algorithm. We then test samples drawn from these proposal densities with the particle filtering estimates based on the Bayesian recursive estimation formulation with the Metropolis Hastings algorithm, which is a well-known Markov chain Monte Carlo method (MCMC). This multiple hypothesis testing algorithm using MCMC in particle filtering helps the filter to explore the state space more effectively in the direction of the degradations. We extended this algorithm for degradation detection and isolation to complete the degradation monitoring framework. We successfully tested our algorithms in degradation monitoring of balance of plant of a boiling water reactor.
Nonparametric inference of network structure and dynamics
NASA Astrophysics Data System (ADS)
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among websites, voting correlations among politicians, co-occurrence of disease-causing genes and others.
Heddam, Salim
2014-01-01
In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling. PMID:24705953
Towards General Algorithms for Grammatical Inference
NASA Astrophysics Data System (ADS)
Clark, Alexander
Many algorithms for grammatical inference can be viewed as instances of a more general algorithm which maintains a set of primitive elements, which distributionally define sets of strings, and a set of features or tests that constrain various inference rules. Using this general framework, which we cast as a process of logical inference, we re-analyse Angluin's famous lstar algorithm and several recent algorithms for the inference of context-free grammars and multiple context-free grammars. Finally, to illustrate the advantages of this approach, we extend it to the inference of functional transductions from positive data only, and we present a new algorithm for the inference of finite state transducers.
Baldwin, W.E.; Morton, R.A.; Putney, T.R.; Katuna, M.P.; Harris, M.S.; Gayes, P.T.; Driscoll, N.W.; Denny, J.F.; Schwab, W.C.
2006-01-01
Several generations of the ancestral Pee Dee River system have been mapped beneath the South Carolina Grand Strand coastline and adjacent Long Bay inner shelf. Deep boreholes onshore and high-resolution seismic-reflection data offshore allow for reconstruction of these paleochannels, which formed during glacial lowstands, when the Pee Dee River system incised subaerially exposed coastal-plain and continental-shelf strata. Paleochannel groups, representing different generations of the system, decrease in age to the southwest, where the modern Pee Dee River merges with several coastal-plain tributaries at Winyah Bay, the southern terminus of Long Bay. Positions of the successive generational groups record a regional, southwestward migration of the river system that may have initiated during the late Pliocene. The migration was primarily driven by barrier-island deposition, resulting from the interaction of fluvial and shoreline processes during eustatic highstands. Structurally driven, subsurface paleotopography associated with the Mid-Carolina Platform High has also indirectly assisted in forcing this migration. These results provide a better understanding of the evolution of the region and help explain the lack of mobile sediment on the Long Bay inner shelf. Migration of the river system caused a profound change in sediment supply during the late Pleistocene. The abundant fluvial source that once fed sand-rich barrier islands was cut off and replaced with a limited source, supplied by erosion and reworking of former coastal deposits exposed at the shore and on the inner shelf.
Inference for modulated stationary processes
Zhao, Zhibiao; Li, Xiaoye
2012-01-01
We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods for stationary or locally stationary time series are not applicable. Based on a self-normalization technique, we address several inference problems, including self-normalized central limit theorem, self-normalized cumulative sum test for the change-point problem, long-run variance estimation through blockwise self-normalization, and self-normalization-based wild boot-strap. Monte Carlo simulation studies show that the proposed self-normalization-based methods outperform stationarity-based alternatives. We demonstrate the proposed methodology using two real data sets: annual mean precipitation rates in Seoul during 1771–2000, and quarterly U.S. Gross National Product growth rates during 1947–2002. PMID:23539557
Network Plasticity as Bayesian Inference
Legenstein, Robert; Maass, Wolfgang
2015-01-01
General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling. PMID:26545099
Quantum inference on Bayesian networks
NASA Astrophysics Data System (ADS)
Low, Guang Hao; Yoder, Theodore J.; Chuang, Isaac L.
2014-06-01
Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values e of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time O (nmP(e)-1), depending critically on P (e), the probability that the evidence might occur in the first place. By implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking O (n2mP(e)-1/2) time per sample. We exploit the Bayesian network's graph structure to efficiently construct a quantum state, a q-sample, representing the intended classical distribution, and also to efficiently apply amplitude amplification, the source of our speedup. Thus, our speedup is notable as it is unrelativized—we count primitive operations and require no blackbox oracle queries.
Statistical learning and selective inference
Taylor, Jonathan; Tibshirani, Robert J.
2015-01-01
We describe the problem of “selective inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis. PMID:26100887
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326
Bayesian Estimation and Inference Using Stochastic Electronics
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326
Inference for interacting linear waves in ordered and random media
NASA Astrophysics Data System (ADS)
Tyagi, P.; Pagnani, A.; Antenucci, F.; Ibánez Berganza, M.; Leuzzi, L.
2015-05-01
A statistical inference method is developed and tested for pairwise interacting systems whose degrees of freedom are continuous angular variables, such as planar spins in magnetic systems or wave phases in optics and acoustics. We investigate systems with both deterministic and quenched disordered couplings on two extreme topologies: complete and sparse graphs. To match further applications in optics also complex couplings and external fields are considered and general inference formulas are derived for real and imaginary parts of Hermitian coupling matrices from real and imaginary parts of complex correlation functions. The whole procedure is, eventually, tested on numerically generated correlation functions and local magnetizations by means of Monte Carlo simulations.
NASA Astrophysics Data System (ADS)
Martin, A. M.; Van Orman, J. A.; Hauck, S. A., II; Sun, N.; Yu, T.; Wang, Y.
2014-12-01
Based upon the high pressure melting temperatures in the Fe-FeS system, an iron "snow" process has been suggested to occur in Mercury's core. However, recent results from the MESSENGER mission indicate very reducing conditions in Mercury, under which a substantial amount of silicon should also dissolve into the core. The presence of Si can significantly modify the chemical and physical properties of Mercury's core (e.g., phase relations, crystallization, density). Moreover, up to 4 wt% C could have been incorporated into the core during the planet formation. In order to test the iron snow hypothesis in a system that is likely to be closer to the actual core composition, we performed in situ high-pressure, high-temperature experiments in the Fe-FeS-Fe2Si-Fe3C system using a multi-anvil press on a synchrotron (Advanced Photon Source, Argonne). To observe low degree eutectic melting, we separated the samples in two parts: (1) an iron rod presaturated with Si and C and (2) a mixture of FeS, Fe2Si and Fe3C. Eutectic melting temperature and phase relations were determined at various pressures between 4.5 and 15.5 GPa using energy dispersive X-ray diffraction and imaging. Temperature was quenched soon after melting in order to preserve the eutectic melt composition. The X-ray images, diffraction spectra and back-scattered electron images of the recovered samples show that eutectic melting occurs in the range of 800 - 900°C in all our experiments. These temperatures are close to the eutectic temperatures in the Fe-FeS-Fe3C system, indicating that Si does not change the eutectic temperatures significantly. Melting therefore occurs at much lower temperature than suggested for the Fe-S-Si system at similar pressures. This difference may be explained by the presence of C and by the higher silicon content in our starting composition. Our experimental setup may also be more suitable for detecting the low degrees of melting in metallic systems. Such low eutectic melting temperatures imply that the iron "snow" process may still be valid even if silicon and carbon are present in Mercury's core.
An Introduction to Causal Inference*
Pearl, Judea
2010-01-01
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. PMID:20305706
NASA Astrophysics Data System (ADS)
Ali, Adam Ahmed; Roiron, Paul; Chabal, Lucie; Ambert, Paul; Gasco, Jean; André, Joël; Terral, Jean-Frédéric
2008-06-01
A geobotanical study of the travertine system of Saint-Guilhem-le-Désert (southern France) was carried out in order to reconstruct the local Holocene environment in a region where the postglacial vegetation history is poorly documented. The travertinisation process has started at ca. 8500 cal. BP, in a landscape dominated by Pinus sylvestris type (probably Pinus nigra sub sp. salzmannii). Around 7000 cal. BP, the travertine system recorded torrential events not evidenced at the regional scale, showing the particularity of the Verdus hydrological regime. More recently, ca. 5100 cal. BP, a lake or a marsh was filled within the Verdus plain, as attested to by sand and silt particles accumulated in the sequence. The present-day vegetation dominated by Quercus ilex, on south facing slopes, was most likely established between the Bronze Age and the Gallo-Roman period correlatively to the decline of Pinus nigra and deciduous Quercus, most probably under human influence.
Bauer, Raymond T.; Okuno, Junji; Thiel, Martin
2014-01-01
Abstract Sexual dimorphism in body size and weaponry was examined in two Cinetorhynchus shrimp species in order to formulate hypotheses on their sexual and mating systems. Collections of Cinetorhynchus sp. A and Cinetorhynchus sp. B were made in March, 2011 on Coconut Island, Hawaii, by hand dipnetting and minnow traps in coral rubble bottom in shallow water. Although there is overlap in male and female size, some males are much larger than females. The major (pereopod 1) chelipeds of males are significantly larger and longer than those of females. In these two Cinetorhynchus species, males and females have third maxillipeds of similar relative size, i.e., those of males are not hypertrophied and probably not used as spear-like weapons as in some other rhynchocinetid (Rhynchocinetes) species. Major chelae of males vary with size, changing from typical female-like chelae tipped with black corneous stout setae to subchelate or prehensile appendages in larger males. Puncture wounds or regenerating major chelipeds were observed in 26.1 % of males examined (N = 38 including both species). We interpret this evidence on sexual dimorphism as an indication of a temporary male mate guarding or “neighborhoods of dominance” mating system, in which larger dominant robustus males defend females and have greater mating success than smaller males. Fecundity of females increased with female size, as in most caridean species (500–800 in Cinetorhynchus sp. A; 300–3800 in Cinetorhynchus sp. B). Based on the sample examined, we conclude that these two species have a gonochoric sexual system (separate sexes) like most but not all other rhynchocinetid species in which the sexual system has been investigated. PMID:25561837
Kumagai, H.; Chouet, B.A.; Nakano, M.
2002-01-01
We present a detailed description of temporal variations in the complex frequencies of long-period (LP) events observed at Kusatsu-Shirane Volcano. Using the Sompi method, we analyze 35 LP events that occurred during the period from August 1992 through January 1993. The observed temporal variations in the complex frequencies can be divided into three periods. During the first period the dominant frequency rapidly decreases from 5 to 1 Hz, and Q of the dominant spectral peak remains roughly constant with an average value near 100. During the second period the dominant frequency gradually increases up to 3 Hz, and Q gradually decreases from 160 to 30. During the third period the dominant frequency increases more rapidly from 3 to 5 Hz, and Q shows an abrupt increase at the beginning of this period and then remains roughly constant with an average value near 100. Such temporal variations can be consistently explained by the dynamic response of a hydrothermal crack to a magmatic heat pulse. During the first period, crack growth occurs in response to the overall pressure increase in the hydrothermal system caused by the heat pulse. Once crack formation is complete, heat gradually changes the fluid in the crack from a wet misty gas to a dry gas during the second period. As heating of the hydrothermal system gradually subsides, the overall pressure in this system starts to decrease, causing the collapse of the crack during the third period.
NASA Astrophysics Data System (ADS)
Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt
2016-01-01
The abundances of 92Nb and 146Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of 53Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for 92Nb and 53Mn cannot be found within the current uncertainties and requires the 92Nb/92Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for 92Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ˜10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings.
Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt
2016-01-26
The abundances of (92)Nb and (146)Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of (53)Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for (92)Nb and (53)Mn cannot be found within the current uncertainties and requires the (92)Nb/(92)Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for (92)Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ∼ 10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings. PMID:26755600
Self-enforcing Private Inference Control
NASA Astrophysics Data System (ADS)
Yang, Yanjiang; Li, Yingjiu; Weng, Jian; Zhou, Jianying; Bao, Feng
Private inference control enables simultaneous enforcement of inference control and protection of users' query privacy. Private inference control is a useful tool for database applications, especially when users are increasingly concerned about individual privacy nowadays. However, protection of query privacy on top of inference control is a double-edged sword: without letting the database server know the content of user queries, users can easily launch DoS attacks. To assuage DoS attacks in private inference control, we propose the concept of self-enforcing private inference control, whose intuition is to force users to only make inference-free queries by enforcing inference control themselves; otherwise, penalty will inflict upon the violating users.
NASA Technical Reports Server (NTRS)
Reichle, Rolf H.; De Lannoy, Gabrielle J. M.
2012-01-01
The Soil Moisture and Ocean Sali