Seizure prediction using adaptive neuro-fuzzy inference system.
Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza
2013-01-01
In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. PMID:24110134
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)
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.
NASA Astrophysics Data System (ADS)
Yurdusev, Mehmet Ali; Firat, Mahmut
2009-02-01
SummaryIn this study, an adaptive neuro fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors including average monthly water bill, population, number of households, gross national product, monthly average temperature observed, monthly total rainfall, monthly average humidity observed and inflation rate. Water consumption modeling in this way will be more consistent than doing it using a single variable as more effective parameter could be incorporated. The ANFIS system is applied to modeling monthly water consumptions of Izmir, Turkey. The results indicated that ANFIS can be successfully applied for monthly water consumption modeling.
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
Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; 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
NASA Astrophysics Data System (ADS)
Ja'fari, Ahmad; Kadkhodaie-Ilkhchi, Ali; Sharghi, Yoosef; Ghanavati, Kiarash
2012-02-01
Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.
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
Heddam, Salim
2014-01-01
This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling. PMID:24057665
Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.
Ubeyli, Elif Derya
2009-10-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261
Adaptive neuro-fuzzy inference system for analysis of Doppler signals.
Ubeyli, Elif Derya
2006-01-01
In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The ophthalmic arterial Doppler signals were recorded from 128 subjects that 62 of them had suffered from ophthalmic artery stenosis and the rest of them had been healthy subjects. Some conclusions concerning the impacts of features on the detection of ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies (total classification accuracy was 97.59%) and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis. PMID:17945697
Human action recognition using meta-cognitive neuro-fuzzy inference system.
Subramanian, K; Suresh, S
2012-12-01
We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort. PMID:23186277
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
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)
Zoveidavianpoor, Mansoor; Samsuri, Ariffin; Shadizadeh, Seyed Reza
2013-02-01
Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir.
Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.
Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L
2012-01-01
Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592
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 (VO2) 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
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.
Ubeyli, Elif Derya
2009-03-01
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286
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.
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.
NASA Astrophysics Data System (ADS)
Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.
2015-01-01
The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and
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.
Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson
2013-01-01
The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs. PMID:24109851
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.
NASA Astrophysics Data System (ADS)
Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.
The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.
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
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
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
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
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
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.
Jhin, Changho; Hwang, Keum Taek
2014-01-01
Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627
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
Jhin, Changho; Hwang, Keum Taek
2014-01-01
Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627
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
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.
NASA Astrophysics Data System (ADS)
Teimouri, Reza; Sohrabpoor, Hamed
2013-12-01
Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM 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
Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail
2013-01-01
Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in
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
NASA Astrophysics Data System (ADS)
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2016-08-01
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
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)
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.
NASA Astrophysics Data System (ADS)
Ajay Kumar, M.; Srikanth, N. V.
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.
NASA Astrophysics Data System (ADS)
Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe
2013-12-01
The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.
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
Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin
2014-06-01
A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. PMID:24638918
NASA Astrophysics Data System (ADS)
Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika
2014-08-01
The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.
Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin
2014-01-01
Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. PMID:24566279
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.
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)
Iphar, Melih; Yavuz, Mahmut; Ak, Hakan
2008-11-01
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.
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.
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)
Kentel, E.; Dogulu, N.
2015-12-01
In Turkey the experience and data required for a hydrological model setup is limited and very often not available. Moreover there are many ungauged catchments where there are also many planned projects aimed at utilization of water resources including development of existing hydropower potential. This situation makes runoff prediction at locations with lack of data and ungauged locations where small hydropower plants, reservoirs, etc. are planned an increasingly significant challenge and concern in the country. Flow duration curves have many practical applications in hydrology and integrated water resources management. Estimation of flood duration curve (FDC) at ungauged locations is essential, particularly for hydropower feasibility studies and selection of the installed capacities. In this study, we test and compare the performances of two methods for estimating FDCs in the Western Black Sea catchment, Turkey: (i) FDC based on Map Correlation Method (MCM) flow estimates. MCM is a recently proposed method (Archfield and Vogel, 2010) which uses geospatial information to estimate flow. Flow measurements of stream gauging stations nearby the ungauged location are the only data requirement for this method. This fact makes MCM very attractive for flow estimation in Turkey, (ii) Adaptive Neuro-Fuzzy Inference System (ANFIS) is a data-driven method which is used to relate FDC to a number of variables representing catchment and climate characteristics. However, it`s ease of implementation makes it very useful for practical purposes. Both methods use easily collectable data and are computationally efficient. Comparison of the results is realized based on two different measures: the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE) value. Ref: Archfield, S. A., and R. M. Vogel (2010), Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi:10.1029/2009WR008481.
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 μm CMOS standard technology.
Adaptive neuro-fuzzy estimation of optimal lens system parameters
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
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.
NASA Astrophysics Data System (ADS)
Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed
2016-06-01
In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.
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
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.
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.
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
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
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
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-10-01
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
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
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
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.
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
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
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
Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design.
Cheng, Yi-Chang; Hsu, Yung-Chi; Lin, Sheng-Fuu
2010-07-01
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
Position control of ionic polymer metal composite actuator based on neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Nguyen, Truong-Thinh; Yang, Young-Soo; Oh, Il-Kwon
2009-07-01
This paper describes the application of Neuro-Fuzzy techniques for controlling an IPMC cantilever configuration under water to improve tracking ability for an IPMC actuator. The controller was designed using an Adaptive Neuro-Fuzzy Controller (ANFC). The measured input data based including the tip-displacements and electrical signals have been recorded for generating the training in the ANFC. These data were used for training the ANFC to adjust the membership functions in the fuzzy control algorithm. The comparison between actual and reference values obtained from the ANFC gave satisfactory results, which showed that Adaptive Neuro-Fuzzy algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFC performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.
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 (
Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm
NASA Astrophysics Data System (ADS)
Hong, Yoon-Seok Timothy; White, Paul A.
2009-01-01
This paper introduces the dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system with on-line and local learning algorithm for complex dynamic hydrological modeling tasks. Our DNFLMS is aimed to implement a fast training speed with the capability of on-line simulation where model adaptation occurs at the arrival of each new item of hydrological data. The DNFLMS applies an on-line, one-pass, training procedure to create and update fuzzy local models dynamically. The extended Kalman filtering algorithm is then implemented to optimize the parameters of the consequence part of each fuzzy model during the training phase. Local generalization in the DNFLMS is employed to optimize the parameters of each fuzzy model separately, region-by-region, using subsets of training data rather than all training data. The proposed DNFLMS is applied to develop a model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatt Cobb hydropower station on spring flow. It is demonstrated that the proposed DNFLMS is superior in terms of model complexity and computational efficiency when compared with models that adopt global generalization such as a multi-layer perceptron (MLP) trained with the back propagation learning algorithm and the well-known adaptive neural-fuzzy system (ANFIS).
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.
Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A
2012-01-01
Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575
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
A Neuro-Fuzzy based System for Classification of Natural Textures
NASA Astrophysics Data System (ADS)
Jiji, G. Wiselin
2016-06-01
A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.
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
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.
Fatouros, Dimitrios G; Nielsen, Flemming Seier; Douroumis, Dionysios; Hadjileontiadis, Leontios J; Mullertz, Anette
2008-08-01
The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations. PMID:18367386
Adaptive Neuro-fuzzy approach in friction identification
NASA Astrophysics Data System (ADS)
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
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.
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
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
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
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…
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.
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
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.
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
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
Favieiro, Gabriela W; Balbinot, Alexandre
2011-01-01
The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects. PMID:22256169
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)
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
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).
Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge.
Cakmakci, Mehmet
2007-09-01
Modelling of anaerobic digestion systems is difficult because their performance is complex and varies significantly with influent characteristics and operational conditions. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for modelling of anaerobic digestion system of primary sludge of Kayseri municipal WasteWater Treatment Plant (WWTP). Effluent Volatile Solid (VS) and methane yield were predicted by the ANFIS. Two stage models were performed. In the first stage, effluent VS concentration was predicted using pH, VS concentration, flowrate of pre-thickened sludge and temperature of the influent as input parameters. In the second stage, effluent VS concentration in addition to first stage input parameters were used as input parameters to predict methane yield. The low Root Mean Square Error (RMSE) and high Index of agreement (IA) values were obtained with subtractive clustering method of a first order Sugeno type inference. The model performance was evaluated with statistical parameters. According to statistical evaluations, the models satisfactorily predict effluent VS concentration and methane yield. PMID:17593401
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
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.
NASA Astrophysics Data System (ADS)
Bilgehan, Mahmut
2011-03-01
In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the evaluation of relationships between concrete compressive strength and ultrasonic pulse velocity (UPV) values using the experimental data obtained from many cores taken from different reinforced concrete structures having different ages and unknown ratios of concrete mixtures. A comparative study is made using the neural nets and neuro-fuzzy (NF) techniques. Statistic measures were used to evaluate the performance of the models. Comparing of the results, it is found that the proposed ANFIS architecture with Gaussian membership function is found to perform better than the multilayer feed-forward ANN learning by backpropagation algorithm. The final results show that especially the ANFIS modelling may constitute an efficient tool for prediction of the concrete compressive strength. Architectures of the ANFIS and neural network established in the current study perform sufficiently in the estimation of concrete compressive strength, and particularly ANFIS model estimates closely follow the desired values. Both ANFIS and ANN techniques can be used in conditions where too many structures are to be examined in a restricted time. The presented approaches enable to practically find concrete strengths in the existing reinforced concrete structures, whose records of concrete mixture ratios are not available or present. Thus, researchers can easily evaluate the compressive strength of concrete specimens using UPV and density values. These methods also contribute to a remarkable reduction in the computational time without any significant loss of accuracy. A comparison of the results clearly shows that particularly the NF approach can be used effectively to predict the compressive strength of concrete using UPV and density values. In addition, these model architectures can be used as a nondestructive procedure for health monitoring of
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
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Nikolić, Vlastimir; Milovančević, Miloš; Lazov, Lyubomir
2016-07-01
Heat affected zone (HAZ) of the laser cutting process may be developed on the basis on combination of different factors. In this investigation was analyzed the HAZ forecasting based on the different laser cutting parameters. The main aim in this article was to analyze the influence of three inputs on the HAZ of the laser cutting process. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for HAZ forecasting. Three inputs are considered: laser power, cutting speed and gas pressure. According the results the cutting speed has the highest influence on the HAZ forecasting (RMSE: 0.0553). Gas pressure has the smallest influence on the HAZ forecasting (RMSE: 0.0801). The results can be used in order to simplify HAZ prediction and analyzing.
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.
Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.
Cosma, Georgina; Acampora, Giovanni; Brown, David; Rees, Robert C; Khan, Masood; Pockley, A Graham
2016-01-01
The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR
Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model
Acampora, Giovanni; Brown, David; Rees, Robert C.
2016-01-01
The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR
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.
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)
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.
Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang
2009-01-01
Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The study demonstrated a
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
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
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
An intelligent load shedding scheme using neural networks and neuro-fuzzy.
Haidar, Ahmed M A; Mohamed, Azah; Al-Dabbagh, Majid; Hussain, Aini; Masoum, Mohammad
2009-12-01
Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper. PMID:20039470
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.
Synchronous Pipeline Circuit Design for an Adaptive Neuro-fuzzy Network
NASA Astrophysics Data System (ADS)
Lin, Che-Wei; Wang, Jeen-Shing; Yu, Chun-Chang; Chen, Ting-Yu
This paper presents an efficient synchronous pipeline hardware implementation procedure for a neuro-fuzzy (NF) circuit. We decompose the NF circuit into a feedforward circuit and a backpropagation circuit. The concept of pre-calculation to share computation results between the feedforward circuit and backpropagation circuit is introduced to achieve a high throughput rate and low resource usage. A novel pipeline architecture has been adopted to fulfill the concept of pre-calculation. With the unique pipeline architecture, we have successfully enhanced the throughput rate and resource sharing between modules. Particularly, the multiplier usage has been reduced from 7 to 3 and the divider usage from 3 to 1. Finally, we have implemented the NF circuit on FGPA. Our experimental results show a superior performance than that of an asynchronous pipeline design approach and the NF system implemented on MATLAB®.
Alavandar, Srinivasan; Nigam, M J
2009-10-01
Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller. PMID:19523623
NASA Astrophysics Data System (ADS)
Sdao, F.; Lioi, D. S.; Pascale, S.; Caniani, D.; Mancini, I. M.
2013-02-01
The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the
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.
NASA Astrophysics Data System (ADS)
Nikolić, Vlastimir; Petković, Dalibor; Lazov, Lyubomir; Milovančević, Miloš
2016-07-01
Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R2 = 0.9653). The worst prediction was observed for dross area per unit length (R2 = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter.
NASA Astrophysics Data System (ADS)
Hoell, Simon; Omenzetter, Piotr
2016-04-01
Fueled by increasing demand for carbon neutral energy, erections of ever larger wind turbines (WTs), with WT blades (WTBs) with higher flexibilities and lower buckling capacities lead to increasing operation and maintenance costs. This can be counteracted with efficient structural health monitoring (SHM), which allows scheduling maintenance actions according to the structural state and preventing dramatic failures. The present study proposes a novel multi-step approach for vibration-based structural damage localization and severity estimation for application in operating WTs. First, partial autocorrelation coefficients (PACCs) are estimated from vibrational responses. Second, principal component analysis is applied to PACCs from the healthy structure in order to calculate scores. Then, the scores are ranked with respect to their ability to differentiate different damage scenarios. This ranking information is used for constructing hierarchical adaptive neuro-fuzzy inference systems (HANFISs), where cross-validation is used to identify optimal numbers of hierarchy levels. Different HANFISs are created for the purposes of structural damage localization and severity estimation. For demonstrating the applicability of the approach, experimental data are superimposed with signals from numerical simulations to account for characteristics of operational noise. For the physical experiments, a small scale WTB is excited with a domestic fan and damage scenarios are introduced non-destructively by attaching small masses. Numerical simulations are also performed for a representative fully functional small WT operating in turbulent wind. The obtained results are promising for future applications of vibration-based SHM to facilitate improved safety and reliability of WTs at lower costs.
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.
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
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.
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
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
Performance analysis of electronic power transformer based on neuro-fuzzy controller.
Acikgoz, Hakan; Kececioglu, O Fatih; Yildiz, Ceyhun; Gani, Ahmet; Sekkeli, Mustafa
2016-01-01
In recent years, electronic power transformer (EPT), which is also called solid state transformer, has attracted great interest and has been used in place of the conventional power transformers. These transformers have many important functions as high unity power factor, low harmonic distortion, constant DC bus voltage, regulated output voltage and compensation capability. In this study, proposed EPT structure contains a three-phase pulse width modulation rectifier that converts 800 Vrms AC to 2000 V DC bus at input stage, a dual active bridge converter that provides 400 V DC bus with 5:1 high frequency transformer at isolation stage and a three-phase two level inverter that is used to obtain AC output at output stage. In order to enhance dynamic performance of EPT structure, neuro fuzzy controllers which have durable and nonlinear nature are used in input and isolation stages instead of PI controllers. The main aim of EPT structure with the proposed controller is to improve the stability of power system and to provide faster response against disturbances. Moreover, a number of simulation results are carried out to verify EPT structure designed in MATLAB/Simulink environment and to analyze compensation ability for voltage harmonics, voltage flicker and voltage sag/swell conditions. PMID:27588243
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
Improved control configuration of PWM rectifiers based on neuro-fuzzy controller.
Acikgoz, Hakan; Kececioglu, O Fatih; Gani, Ahmet; Yildiz, Ceyhun; Sekkeli, Mustafa
2016-01-01
It is well-known that rectifiers are used widely in many applications required AC/DC transformation. With technological advances, many studies are performed for AC/DC converters and many control methods are proposed in order to improve the performance of these rectifiers in recent years. Pulse width modulation (PWM) based rectifiers are one of the most popular rectifier types. PWM rectifiers have lower input current harmonics and higher power factor compared to classical diode and thyristor rectifiers. In this study, neuro-fuzzy controller (NFC) which has robust, nonlinear structure and do not require the mathematical model of the system to be controlled has been proposed for PWM rectifiers. Three NFCs are used in control scheme of proposed PWM rectifier in order to control the dq-axis currents and DC voltage of PWM rectifier. Moreover, simulation studies are carried out to demonstrate the performance of the proposed control scheme at MATLAB/Simulink environment in terms of rise time, settling time, overshoot, power factor, total harmonic distortion and power quality. PMID:27504240
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.
Introducing an Evolving Local Neuro-Fuzzy Model--Application to modeling of car-following behavior.
Kazemi, Reza; Abdollahzade, Majid
2015-11-01
This paper proposes an Evolving Local Linear Neuro-Fuzzy Model for modeling and identification of nonlinear time-variant systems which change their nature and character over time. The proposed approach evolves through time to follow the structural changes in the time-variant dynamic systems. The evolution process is managed by a distance-based extended hierarchical binary tree algorithm, which decides whether the proposed evolving model should be adapted to the system variations or evolution is necessary. To represent an interesting but challenging example of the systems with changing dynamics, the proposed evolving model is applied to model car-following process in a traffic flow, as an online identification problem. Results of simulations demonstrate effectiveness of the proposed approach in modeling of the time-variant systems. PMID:26410447
EMG-based neuro-fuzzy control of a 4DOF upper-limb power-assist exoskeleton.
Kiguchi, Kazuo; Imada, Yasunobu; Liyanage, Manoj
2007-01-01
We have been developing a 4DOF exoskeleton robot system in order to assist shoulder vertical motion, shoulder horizontal motion, elbow motion, and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The robot is directly attached to a user's body and activated based on EMG (Electromyogram) signals of the user's muscles, since the EMG signals directly reflect the user's motion intention. A neuro-fuzzy controller has been applied to control the exoskeleton robot system. In this paper, controller adaptation method to user's EMG signals is proposed. A motion indicator is introduced to indicate the motion intention of the user for the controller adaptation. The experimental results show the effectiveness of the proposed method. PMID:18002635
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.
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.
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
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.
Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling
NASA Astrophysics Data System (ADS)
Samadzadegan, Farhad; Azizi, Ali; Hahn, Michael; Lucas, Curo
Three-dimensional object recognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper a novel approach for object recognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany.
Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.
Chang, Fi-John; Chung, Chang-Han; Chen, Pin-An; Liu, Chen-Wuing; Coynel, Alexandra; Vachaud, Georges
2014-10-01
We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management. PMID:25046611
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.
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
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.
NASA Astrophysics Data System (ADS)
Kurtulus, Bedri; Razack, Moumtaz
2010-02-01
SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.
Analyzing the dynamics of emotional scene sequence using recurrent neuro-fuzzy network.
Zhang, Qing; Lee, Minho
2013-02-01
In this paper, we propose a new framework to analyze the temporal dynamics of the emotional stimuli. For this framework, both electroencephalography signal and visual information are of great importance. The fusion of visual information with brain signals allows us to capture the users' emotional state. Thus we adopt previously proposed fuzzy-GIST as emotional feature to summarize the emotional feedback. In order to model the dynamics of the emotional stimuli sequence, we develop a recurrent neuro-fuzzy network for modeling the dynamic events of emotional dimensions including valence and arousal. It can incorporate human expertise by IF-THEN fuzzy rule while recurrent connections allow the fuzzy rules of network to see its own previous output. The results show that such a framework can interact with human subjects and generate arbitrary emotional sequences after learning the dynamics of an emotional sequence with enough number of samples. PMID:24427190
Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm
NASA Astrophysics Data System (ADS)
Bouzaida, Sana; Sakly, Anis; M'Sahli, Faouzi
2014-01-01
This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.
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
Neuro-fuzzy chip to handle complex tasks with analog performance.
de Jesus Navas-Gonzalez, R; Vidal-Verdu, F; Rodriguez-Vazquez, A
2003-01-01
This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input-output delay, and precision, performs as a fully analog implementation. However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core. Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture are smaller than those of its purely analog counterparts simply because most rules are implemented through programming. The paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype. This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 /spl mu/m standard technology. It has two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided. PMID:18244584
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.
NASA Astrophysics Data System (ADS)
Andò, Bruno; Carbone, Daniele
2004-05-01
Gravity measurements are utilized at active volcanoes to detect mass changes linked to magma transfer processes and thus to recognize forerunners to paroxysmal volcanic events. Continuous gravity measurements are now increasingly performed at sites very close to active craters, where there is the greatest chance to detect meaningful gravity changes. Unfortunately, especially when used against the adverse environmental conditions usually encountered at such places, gravimeters have been proved to be affected by meteorological parameters, mainly by changes in the atmospheric temperature. The pseudo-signal generated by these perturbations is often stronger than the signal generated by actual changes in the gravity field. Thus, the implementation of well-performing algorithms for reducing the gravity signal for the effect of meteorological parameters is vital to obtain sequences useful from the volcano surveillance standpoint. In the present paper, a Neuro-Fuzzy algorithm, which was already proved to accomplish the required task satisfactorily, is tested over a data set from three gravimeters which worked continuously for about 50 days at a site far away from active zones, where changes due to actual fluctuation of the gravity field are expected to be within a few microgal. After accomplishing the reduction of the gravity series, residuals are within about 15 μGal peak-to-peak, thus confirming the capabilities of the Neuro-Fuzzy algorithm under test of performing the required task satisfactorily.
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.
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.
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.
A mathematical model of neuro-fuzzy approximation in image classification
NASA Astrophysics Data System (ADS)
Gopalan, Sasi; Pinto, Linu; Sheela, C.; Arun Kumar M., N.
2016-06-01
Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.
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
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.
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
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
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.
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.
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.
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.
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.
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
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.
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.
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.
NASA Astrophysics Data System (ADS)
Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi
2016-09-01
This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.
INFeRS: Interactive Numeric Files Retrieval System. Final Report.
ERIC Educational Resources Information Center
Chiang, Katherine; And Others
In 1988 Mann Library at Cornell University proposed to develop a computer system that would support interactive access to significant electronic files in agriculture and the life sciences. This system was titled the Interactive Numeric Files Retrieval System (INFeRS). This report describes how project goals were met and it presents the project's…
Magma reservoir systems inferred from tilt patterns
NASA Astrophysics Data System (ADS)
Schimozuru, D.
1981-09-01
Inflation patterns based on water-tube tiltmeter and levelling observation show different features for Krafla Volcano in Iceland and Kilauea Volcano in Hawaii. Monotonous sawtooth shape inflation is observed at Krafla, while inflation curves at Kileauea are more or less complicated. The difference was attributed to differences in the system of magma reservoir for the two volcanoes. By using the electrical equivalent of a magma reservoir and volcanic conduit as a capacitor and a resistor, an electrical oseillator was considered to be a possible model for a magma reservoir system. In the case of Krafla, the magma reservoir system is replaced with one electric oscillator called «Single system» or «Icelandic type» system. The complicated inflation pattern of Kilauea was interpreted as the assembly of a main magma reservoir and the group of surrounding small reservoirs. The equivalent electric analogue is the composite parallel and serial connection of a single oscillator which generates irregular output voltage during a charging process. The proposed magma reservoir system of Kilauea is called «Multi-coupled system» or «Hawaiian type system» which also help in interpreting the wondering of the uplift center and tidal phenomena of the Halemaumau lava lake.
Fuzzy exemplar-based inference system for flood forecasting
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin
2005-02-01
Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on a fuzzy inference system, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.
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
NASA Astrophysics Data System (ADS)
Nadhir, Ahmad; Naba, Agus; Hiyama, Takashi
An optimal control for maximizing extraction of power in variable-speed wind energy conversion system is presented. Intelligent gradient detection by fuzzy inference system (FIS) in maximum power point tracking control is proposed to achieve power curve operating near optimal point. Speed rotor reference can be adjusted by maximum power point tracking fuzzy controller (MPPTFC) such that the turbine operates around maximum power. Power curve model can be modelled by using adaptive neuro fuzzy inference system (ANFIS). It is required to simply well estimate just a few number of maximum power points corresponding to optimum generator rotor speed under varying wind speed, implying its training can be done with less effort. Using the trained fuzzy model, some estimated maximum power points as well as their corresponding generator rotor speed and wind speed are determined, from which a linear wind speed feedback controller (LWSFC) capable of producing optimum generator speed can be obtained. Applied to a squirrel-cage induction generator based wind energy conversion system, MPPTFC and LWSFC could maximize extraction of the wind energy, verified by a power coefficient stay at its maximum almost all the time and an actual power line close to a maximum power efficiency line reference.
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.
NASA Astrophysics Data System (ADS)
Nebot, Àngela; Mugica, Francisco
2012-10-01
Fuzzy inductive reasoning (FIR) is a modelling and simulation methodology derived from the General Systems Problem Solver. It compares favourably with other soft computing methodologies, such as neural networks, genetic or neuro-fuzzy systems, and with hard computing methodologies, such as AR, ARIMA, or NARMAX, when it is used to predict future behaviour of different kinds of systems. This paper contains an overview of the FIR methodology, its historical background, and its evolution.
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.
Malik, Owais A; Senanayake, S M N Arosha; Zaheer, Dansih
2015-03-01
An intelligent recovery evaluation system is presented for objective assessment and performance monitoring of anterior cruciate ligament reconstructed (ACL-R) subjects. The system acquires 3-D kinematics of tibiofemoral joint and electromyography (EMG) data from surrounding muscles during various ambulatory and balance testing activities through wireless body-mounted inertial and EMG sensors, respectively. An integrated feature set is generated based on different features extracted from data collected for each activity. The fuzzy clustering and adaptive neuro-fuzzy inference techniques are applied to these integrated feature sets in order to provide different recovery progress assessment indicators (e.g., current stage of recovery, percentage of recovery progress as compared to healthy group, etc.) for ACL-R subjects. The system was trained and tested on data collected from a group of healthy and ACL-R subjects. For recovery stage identification, the average testing accuracy of the system was found above 95% (95-99%) for ambulatory activities and above 80% (80-84%) for balance testing activities. The overall recovery evaluation performed by the proposed system was found consistent with the assessment made by the physiotherapists using standard subjective/objective scores. The validated system can potentially be used as a decision supporting tool by physiatrists, physiotherapists, and clinicians for quantitative rehabilitation analysis of ACL-R subjects in conjunction with the existing recovery monitoring systems. PMID:24801517
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.
Fuzzy system reliability computation of the convoy of unmanned intelligent vehicles
NASA Astrophysics Data System (ADS)
Singh, Harpreet; Dixit, Arati M.; Mustapha, Adam; Gerhart, Grant R.
2008-10-01
Unmanned intelligent ground vehicles play significant role in wide range of applications. They are of great significance in military applications as well as other commercial applications. In order to assure the performance of unmanned intelligent vehicles, it is important to predict the reliability of the system. Reliability can be calculated using different approaches as seen in the literature, but we propose a Graph theoretic approach supported by Fuzzy and Neuro-Fuzzy approaches for predicting the node and branch reliability of the system. We portray the convoy of unmanned vehicles as a communication network where the nodes represent the station of the convoy of unmanned intelligent vehicles and the branches would represent the path between two stations. The node and branch reliability is calculated using the Fuzzy and Neuro Fuzzy approaches. The terminal and system reliability would be calculated using Boolean algebra. Thus the overall system reliability of a convoy of vehicles is the result of Fuzzy, Neuro-Fuzzy and Boolean approaches. A spanning tree based algorithm is proposed for computation of the system reliability of a convoy of vehicles. We also propose to simulate the overall system reliability with some existing data of factors that contribute in computation of node and branch reliability.
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.
Hybrid soft computing systems for reservoir PVT properties prediction
NASA Astrophysics Data System (ADS)
Khoukhi, Amar
2012-07-01
In reservoir engineering, the knowledge of Pressure-Volume-Temperature (PVT) properties is of great importance for many uses, such as well test analyses, reserve estimation, material balance calculations, inflow performance calculations, fluid flow in porous media and the evaluation of new formations for the potential development and enhancement oil recovery projects. The determination of these properties is a complex problem because laboratory-measured properties of rock samples ("cores") are only available from limited and isolated well locations and/or intervals. Several correlation models have been developed to relate these properties to other measures which are relatively abundant. These models include empirical correlations, statistical regression and artificial neural networks (ANNs). In this paper, a comprehensive study is conducted on the prediction of the bubble point pressure and oil formation volume factor using two hybrid of soft computing techniques; a genetically optimised neural network and a genetically enhanced subtractive clustering for the parameter identification of an adaptive neuro-fuzzy inference system. Simulation experiments are provided, showing the performance of the proposed techniques as compared with commonly used regression correlations, including standard artificial neural networks.
A combined sEMG and accelerometer system for monitoring functional activity in stroke.
Roy, Serge H; Cheng, M Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S Hamid; De Luca, Carlo J
2009-12-01
Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of < 10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. PMID:20051332
A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke.
Roy, S; Cheng, M; Chang, S; Moore, J; De Luca, G; Nawab, S; De Luca, C
2014-04-23
Remote monitoring of physical activity using bodyworn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data were recorded from 10 hemi paretic patients while they carried out a sequence of 11 activities of daily living (Identification tasks), and 10 activities used to evaluate misclassification errors (non-Identification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the non-Identification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of 4 ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0 %, and a mean specificity of 99.7 % for the identification tasks, and a mean misclassification error of < 10% for the non-Identification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. PMID:24760921
A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke
Roy, Serge H.; Cheng, M. Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S. Hamid; De Luca, Carlo J.
2010-01-01
Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of <10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. PMID:20051332
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 Bézier 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
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.
Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin
2014-01-01
The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file. PMID:24345245
Automatic generation of fuzzy inference systems via unsupervised learning.
Er, Meng Joo; Zhou, Yi
2008-12-01
In this paper, a novel approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating Fuzzy Inference Systems (FISs) is presented. In the EDSGFQL approach, structure identification and parameter estimations of FISs are achieved via Unsupervised Learning (UL) (including Reinforcement Learning (RL)). Instead of using Supervised Learning (SL), UL clustering methods are adopted for input space clustering when generating FISs. At the same time, structure and preconditioning parts of a FIS are generated in a RL manner in that fuzzy rules are adjusted and deleted according to reinforcement signals. The proposed EDSGFQL methodologies can automatically create, delete and adjust fuzzy rules dynamically. Simulation studies on wall-following and obstacle avoidance tasks by a mobile robot show that the proposed approach is superior in generating efficient FISs. PMID:18653313
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
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
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.
Classification of Microarray Data Using Kernel Fuzzy Inference System
Kumar Rath, Santanu
2014-01-01
The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function.
Automatic Road Gap Detection Using Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.
2011-09-01
Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.
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.
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.
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.
Soil disturbance evaluation: application of ANFIS
Technology Transfer Automated Retrieval System (TEKTRAN)
New techniques to understand the relationship of soil components as impacted by management are needed. In this work, an Adaptive Neuro-Fuzzy Inference System (ANFIS) applied for study the contiguous relations between soil disturbed indicators. Several ANFIS surfaces, which described the contiguous ...
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.
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.
An evolutionary approach toward dynamic self-generated fuzzy inference systems.
Zhou, Yi; Er, Meng Joo
2008-08-01
An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods. PMID:18632385
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
NASA Astrophysics Data System (ADS)
Chang, L. C.; Kao, I. F.; Tsai, F. H.; Hsu, H. C.; Yang, S. N.; Shen, H. Y.; Chang, F. J.
2015-12-01
Typhoons and storms hit Taiwan several times every year and cause serious flood disasters. Because the mountainous terrain and steep landform rapidly accelerate the speed of flood flow, rivers cannot be a stable source of water supply. Reservoirs become one of the most important and effective floodwater storage facilities. However, real-time operation for reservoir flood control is a continuous and instant decision-making process based on rules, laws, meteorological nowcast, in addition to the immediate rainfall and hydrological data. The achievement of reservoir flood control can effectively mitigate flood disasters and store floodwaters for future uses. In this study, we construct an intelligent decision support system for reservoir flood control through integrating different types of neural networks and the above information to solve this problem. This intelligent reservoir flood control decision support system includes three parts: typhoon track classification, flood forecast and adaptive water release models. This study used the self-organizing map (SOM) for typhoon track clustering, nonlinear autoregressive with exogenous inputs (NARX) for multi-step-ahead reservoir inflow prediction, and adaptive neuro-fuzzy inference system (ANFIS) for reservoir flood control. Before typhoons landfall, we can estimate the entire flood hydrogragh of reservoir inflow by using SOM and make a pre-release strategy and real-time reservoir flood operating by using ANFIS. In the meanwhile, NARX can be constantly used real-time five-hour-ahead inflow prediction for providing the newest flood information. The system has been successfully implemented Typhoons Trami (2013), Fitow (2013) and Matmo (2014) in Shihmen Reservoir.
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.
Dynamical inference for transitions in stochastic systems with α-stable Lévy noise
NASA Astrophysics Data System (ADS)
Gao, Ting; Duan, Jinqiao; Kan, Xingye; Cheng, Zhuan
2016-07-01
A goal of data assimilation is to infer stochastic dynamical behaviors with available observations. We consider transition phenomena between metastable states for a stochastic system with (non-Gaussian) α -stable Lévy noise. With either discrete time or continuous time observations, we infer such transitions between metastable states by computing the corresponding non-local Zakai equation (and its discrete time counterpart) and examining the most probable orbits for the state system. Examples are presented to demonstrate this approach. This work was partly supported by the NSF Grant 1025422.
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.
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
Hydroclimatic Extremes: Inferences and Prediction from a Dynamical Systems Perspective
NASA Astrophysics Data System (ADS)
Lall, U.
2015-12-01
Hydroclimatic extremes , such as major floods and droughts, or periods with a high frequency of clustered tornadoes, fires or cyclones, have often been thought of as random, rare events, and much of the literature on these topics has been obsessed with the estimation of the tail probabilities (e.g., the 100 year event) of these processes. It has taken the "acceptance" of the notion of climate change to question whether the machinery developed for such estimation or even the associated questions are reasonable. However, much of the literature that has evolved since has focused on how to detect and model changes in these probabilities using a variety of methods. In this talk, I will argue that while such efforts may be useful in a certain, outdated context, they are not necessarily leading to an improvement in eihter the science of the application of the science to disaster risk mitigation. I develop an argument that hydroclimatic extremes result from an organization of the associated global and local dynamical systems that leads to the systems trajectories locking into a particular region of state space. Such excursions could be considered as rare events, in their ultimate expression, or in their frequency of visitation and persistence in those states. An open question is whether the dynamics of the system under such conditions are marked by high or low predictabilty in the Lyapunov sense. A characterization of the dimension and predictability of hydroclimatic extremes would allow us to better understand the potential implications of climate change, and also of whether or not a regional drought or similar persistent regime is likely to dissipate or grow.
MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.
Bucci, Vanni; Tzen, Belinda; Li, Ning; Simmons, Matt; Tanoue, Takeshi; Bogart, Elijah; Deng, Luxue; Yeliseyev, Vladimir; Delaney, Mary L; Liu, Qing; Olle, Bernat; Stein, Richard R; Honda, Kenya; Bry, Lynn; Gerber, Georg K
2016-01-01
Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE's utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. PMID:27259475
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
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)
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.
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
Design of a Software Sensor for Feedwater Flow Measurement Using a Fuzzy Inference System
Na, Man Gyun; Shin, Sun Ho; Jung, Dong Won
2005-06-15
Venturi meters are used to measure the feedwater flow rate in most current pressurized water reactors. These meters can decrease the thermal performance of nuclear power plants because the feedwater flow rate can be overmeasured due to their fouling phenomena that make corrosion products caused by long-term operation accumulate in the feedwater flow meters. Therefore, in this paper, a software sensor using a fuzzy inference system is developed in order to increase the thermal efficiency by accurately estimating online the feedwater flow rate. The fuzzy inference system to be used for black-box modeling of the feedwater system is equipped with an automatic design algorithm that automates the selection of the input signals to the fuzzy inference system and its fuzzy rule generation including parameter optimization. The proposed algorithm was verified by using the numerical simulation data of the MARS code for Kori Nuclear Power Plant Unit 1 and also the real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations using numerical simulation data and real plant data, the relative 2{sigma} errors and the relative maximum error are small enough. The proposed method can be applied successfully to validate and monitor the existing feedwater flow meters.
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.
On the functional equivalence of fuzzy inference systems and spline-based networks.
Hunt, K J; Haas, R; Brown, M
1995-06-01
The conditions under which spline-based networks are functionally equivalent to the Takagi-Sugeno-model of fuzzy inference are formally established. We consider a generalized form of basis function network whose basis functions are splines. The result admits a wide range of fuzzy membership functions which are commonly encountered in fuzzy systems design. We use the theoretical background of functional equivalence to develop a hybrid fuzzy-spline net for inverse dynamic modeling of a hydraulically driven robot manipulator. PMID:7496588
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
Inference system using softcomputing and mixed data applied in metabolic pathway datamining.
Arredondo, Tomás; Candel, Diego; Leiva, Mauricio; Dombrovskaia, Lioubov; Agulló, Loreine; Seeger, Michael
2012-01-01
This paper describes the development of an inference system used for the identification of genes that encode enzymes of metabolic pathways. Input sequence alignment values are used to classify the best candidate genes for inclusion in a metabolic pathway map. The system workflow allows the user to provide feedback, which is stored in conjunction with analysed sequences for periodic retraining. The construction of the system involved the study of several different classifiers with various topologies, data sets and parameter normalisation data models. Experimental results show an excellent prediction capability with the classifiers trained with mixed data providing the best results. PMID:22479819
Combining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu, Vân Anh; Sanguinetti, Guido
2015-01-01
Motivation: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system. These are clearly interpretable and can be used for predictions; however, they rely on strong assumptions and are typically very demanding computationally. Results: Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called ‘jump trees’) to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma. Availability and implementation: Our MATLAB implementation of Jump3 is available at http://homepages.inf.ed.ac.uk/vhuynht/software.html. Contact: vhuynht@inf.ed.ac.uk or G.Sanguinetti@ed.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25573916
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.
Toni, Tina; Welch, David; Strelkowa, Natalja; Ipsen, Andreas; Stumpf, Michael P.H.
2008-01-01
Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus. PMID:19205079
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning.
Er, Meng Joo; Deng, Chang
2004-06-01
This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior. PMID:15484918
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.
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
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
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.
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
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
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.
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.
Singer, R.M.; Gross, K.C. ); Humenik, K.E. . Dept. of Computer Science)
1991-01-01
An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.
Evaluation of a dual processor implementation for a fault inferring nonlinear detection system
NASA Technical Reports Server (NTRS)
Godiwala, P. M.; Caglayan, A. K.; Morrell, F. R.
1987-01-01
The design of a modified fault inferring nonlinear detection system (FINDS) algorithm for a dual-processor configured flight computer is described. The algorithm was changed in order to divide it into its translational dynamics and rotational kinematics and to use it for parallel execution on the flight computer. The FINDS consists of: (1) a no-fail filter (NFF), (2) a set of test-of-mean detection tests, (3) a bank of first order filters to estimate failure levels in individual sensors, and (4) a decision function. NFF filter performance using flight recorded sensor data is analyzed using a filter autoinitialization routine. The failure detection and isolation capability of the partitioned algorithm is evaluated. A multirate implementation for the bias-free and bias filter gain and covariance matrices is discussed.
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
NASA Astrophysics Data System (ADS)
Birk, Steffen; Wagner, Thomas; Mayaud, Cyril
2014-05-01
Threshold behavior in hydrological systems generally involves a qualitative change of a single process, the system response or the functioning of the system. Different types of thresholds and their underlying controls are examined using the example of the Lurbach karst system (Austria). This karst system receives concentrated allogenic recharge from the sinking stream Lurbach, which under low-flow conditions only resurges at the Hammerbach spring. Under medium- to high-flow conditions, however, an overflow toward another spring, the Schmelzbach outlet occurs. The overflow probably is activated when a conduit pathway connecting the two sub-catchments is flooded at a given threshold water level. Unfortunately, the value of this threshold cannot be determined, as information about water levels within this karst system are scarce due to the lack of observation wells and the inaccessibility of relevant cave sections. Yet a corresponding threshold discharge of the Hammerbach spring can be inferred from tracer test results. Interestingly, a tracer test conducted in 2008 suggests that the overflow is activated at a discharge lower than that inferred from tracer tests reported earlier (Wagner et al., EGU2011-7962). In order to better understand this suspected change in the discharge threshold, the physicochemical responses of the Hammerbach spring were analyzed. Applying the concept of process time scales (Birk and Wagner, EGU2013-11365) to the Hammerbach spring suggests that the threshold travel time controlling the response of the spring water temperature was changed in the time period from 2006 to 2009 relative to the years before. At the same time, the Hammerbach spring hydrograph appears to have changed. For instance, the flow duration curve and the master recession curves for the time period from 2006 to 2009 are found to be markedly different from those of earlier time periods. All of these observations can be consistently explained by a reduction of the conduit
Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Toro, Luis; Cavazos, Alberto; Colás, Rafael
2009-12-01
A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.
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
NASA Astrophysics Data System (ADS)
King, Gary; Rosen, Ori; Tanner, Martin A.
2004-09-01
This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.
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
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 focuses 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.
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
Petrov, S.
1996-10-01
Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.
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.
Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid
NASA Astrophysics Data System (ADS)
Salehi, H.; Zeinali-Heris, S.; Esfandyari, M.; Koolivand, M.
2013-04-01
In this study, experiments were performed by six different volume fractions of Al2O3 nanoparticles in distilled water. Then, actual nanofluid Nusslet number compared by Adaptive neuro fuzzy inference system (ANFIS) predicted number in square cross-section duct in laminar flow under uniform heat flux condition. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 0.99 for all cases.
Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion
Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J.; Grima, Ramon; Hasenauer, Jan
2016-01-01
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity. PMID:27447730
Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.
Fröhlich, Fabian; Thomas, Philipp; Kazeroonian, Atefeh; Theis, Fabian J; Grima, Ramon; Hasenauer, Jan
2016-07-01
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity. PMID:27447730
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
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.
ERIC Educational Resources Information Center
Jett-Simpson, Mary
High, middle, and low readers in kindergarten, second, and fourth grades participated in a study of inferential comprehension. The Jett-Simpson Classification System for Verbalized Inference was developed, its reliability was established, and children's open responses to a wordless picture book, "Frog Goes to Dinner," were analyzed using it.…
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
Inferring the Architectures of Planetary Systems from Kepler Results with SysSim
NASA Astrophysics Data System (ADS)
Ford, Eric
. Without a method to interpret and debias the Kepler planet candidates on a system-by- system basis, it is not possible to rigorously address critical NASA-relevant science questions like: 1) What fraction of stars have planets? What fraction of stars have solar system analogs? 2) What is the planetary system environment of potentially habitable planets? 3) What is the expected yield of future NASA exoplanet missions? 4) Are there different populations of planetary systems? What are their architectures? and many other valuable questions that are critical for understanding the origins of solar systems. To fill this critical gap, we have developed the Planetary System Simulator or SysSim, which empirically determines the underlying debiased distribution of planetary properties (e.g., planet size, orbital period, etc.) and planetary system architecture (e.g., relative inclinations, number of planets per star) simultaneously. The earliest version of SysSim measured the exoplanetary inclination distribution for the first time, a finding of major consequence for planet formation theorists (LR+11). We propose to extend SysSim to include new planetary architecture parameters and new observational constraints from the growing Kepler dataset. We will produce rigorously-debiased exoplanetary populations that will improve the understanding of the frequency, architecture, and origins of planetary systems. Our team is uniquely qualified to fulfill these tasks based on our extensive experience with Kepler data, metadata, multi-transiting systems, statistical inference, and previous work (LR+11, B12, Ragozzine & Holman 2010).
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
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.
Ling, Steve S H; Nguyen, Hung T
2011-03-01
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity. PMID:21349796
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
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
Estimating daily pan evaporation using adaptive neural-based fuzzy inference system
NASA Astrophysics Data System (ADS)
Keskin, M. Erol; Terzi, Özlem; Taylan, Dilek
2009-09-01
Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake Eğirdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.
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
A new Multiple ANFIS model for classification of hemiplegic gait.
Yardimci, A; Asilkan, O
2014-01-01
Neuro-fuzzy system is a combination of neural network and fuzzy system in such a way that neural network learning algorithms, is used to determine parameters of the fuzzy system. This paper describes the application of multiple adaptive neuro-fuzzy inference system (MANFIS) model which has hybrid learning algorithm for classification of hemiplegic gait acceleration (HGA) signals. Decision making was performed in two stages: feature extraction using the wavelet transforms (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the HGA signals. PMID:25160151
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.
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
Embedded prediction in feature extraction: application to single-trial EEG discrimination.
Hsu, Wei-Yen
2013-01-01
In this study, an analysis system embedding neuron-fuzzy prediction in feature extraction is proposed for brain-computer interface (BCI) applications. Wavelet-fractal features combined with neuro-fuzzy predictions are applied for feature extraction in motor imagery (MI) discrimination. The features are extracted from the electroencephalography (EEG) signals recorded from participants performing left and right MI. Time-series predictions are performed by training 2 adaptive neuro-fuzzy inference systems (ANFIS) for respective left and right MI data. Features are then calculated from the difference in multi-resolution fractal feature vector (MFFV) between the predicted and actual signals through a window of EEG signals. Finally, the support vector machine is used for classification. The proposed method estimates its performance in comparison with the linear adaptive autoregressive (AAR) model and the AAR time-series prediction of 6 participants from 2 data sets. The results indicate that the proposed method is promising in MI classification. PMID:23248335
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.
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)
Knuth, K. H.
2001-05-01
We consider the application of Bayesian inference to the study of self-organized structures in complex adaptive systems. In particular, we examine the distribution of elements, agents, or processes in systems dominated by hierarchical structure. We demonstrate that results obtained by Caianiello [1] on Hierarchical Modular Systems (HMS) can be found by applying Jaynes' Principle of Group Invariance [2] to a few key assumptions about our knowledge of hierarchical organization. Subsequent application of the Principle of Maximum Entropy allows inferences to be made about specific systems. The utility of the Bayesian method is considered by examining both successes and failures of the hierarchical model. We discuss how Caianiello's original statements suffer from the Mind Projection Fallacy [3] and we restate his assumptions thus widening the applicability of the HMS model. The relationship between inference and statistical physics, described by Jaynes [4], is reiterated with the expectation that this realization will aid the field of complex systems research by moving away from often inappropriate direct application of statistical mechanics to a more encompassing inferential methodology.
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.
Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.
2010-01-01
The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307
Misra, Sudip; Singh, Ranjit; Rohith Mohan, S V
2010-01-01
The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307
Forward-Chaining Versus A Graph Approach As The Inference Engine In Expert Systems
NASA Astrophysics Data System (ADS)
Neapolitan, Richard E.
1986-03-01
Rule-based expert systems are those in which a certain number of IF-THEN rules are assumed to be true. Based on the verity of some assertions, the rules deduce as many new conclusions as possible. A standard technique used to make these deductions is forward-chaining. In forward-chaining, the program or 'inference engine' cycles through the rules. At each rule, the premises for the rule are checked against the current true assertions. If all the premises are found, the conclusion is added to the list of true assertions. At that point it is necessary to start over at the first rule, since the new conclusion may be a premise in a rule already checked. Therefore, each time a new conclusion is deduced it is necessary to start the rule checking procedure over. This process continues until no new conclusions are added and the end of the list of rules is reached. The above process, although quite costly in terms of CPU cycles due to the necessity of repeatedly starting the process over, is necessary if the rules contain 'pattern variables'. An example of such a rule is, 'IF X IS A BACTERIA, THEN X CAN BE TREATED WITH ANTIBIOTICS'. Since the rule can lead to conclusions for many values of X, it is necessary to check each premise in the rule against every true assertion producing an association list to be used in the checking of the next premise. However, if the rule does not contain variable data, as is the case in many current expert systems, then a rule can lead to only one conclusion. In this case, the rules can be stored in a graph, and the true assertions in an assertion list. The assertion list is traversed only once; at each assertion a premise is triggered in all the rules which have that assertion as a premise. When all premises for a rule trigger, the rule's conclusion is added to the END of the list of assertions. It must be added at the end so that it will eventually be used to make further deductions. In the current paper, the two methods are described in
Inferring cortical function in the mouse visual system through large-scale systems neuroscience.
Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof
2016-07-01
The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort. PMID:27382147
Inferring cortical function in the mouse visual system through large-scale systems neuroscience
Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W.; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R. Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof
2016-01-01
The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort. PMID:27382147
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
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
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.
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
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
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
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.
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
2010-01-01
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. PMID:20542910
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.
NASA Technical Reports Server (NTRS)
Sigwarth, John B.; Bekerat, Hamed A.
2008-01-01
The Visible Imaging System (VIS) on the polar spacecraft provided time sequences of auroral images at multiple wavelengths that yield information of auroral dynamics on a global scale with a spatial resolution of - 20 km and temporal resolution of approx. 1 minute. Time sequences of VIS images in which the aurora was highly dynamic are used to infer global maps for the electron precipitation parameters, energy flux and characteristic energies, and ionospheric conductances. The maps are inferred from the corresponding VIS images using an auroral model (Lumerzheim et al., 1987). The temporal and spatial resolution of the VIS inferred patterns are unprecedented. The inferred patterns are highly structured and vary significantly on a time scale of less than 5 minutes. These patterns can be very beneficial for global physics-based numerical models for the high-latitude ionosphere which previously had to rely on statistical models for the electron precipitation and ionospheric conductance.
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
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
A hybrid conceptual-fuzzy inference streamflow modelling for the Letaba River system in South Africa
NASA Astrophysics Data System (ADS)
Katambara, Zacharia; Ndiritu, John G.
There has been considerable water resources developments in South Africa and other regions in the world in order to meet the ever-increasing water demands. These developments have not been matched with a similar development of hydrological monitoring systems and hence there is inadequate data for managing the developed water resources systems. The Letaba River system ( Fig. 1) is a typical case of such a system in South Africa. The available water on this river is over-allocated and reliable daily streamflow modelling of the Letaba River that adequately incorporates the main components and processes would be an invaluable aid to optimal operation of the system. This study describes the development of a calibrated hybrid conceptual-fuzzy-logic model and explores its capability in reproducing the natural processes and human effects on the daily stream flow in the Letaba River. The model performance is considered satisfactory in view of the complexity of the system and inadequacy of relevant data. Performance in modelling streamflow improves towards the downstream and matches that of a stand-alone fuzzy-logic model. The hybrid model obtains realistic estimates of the major system components and processes including the capacities of the farm dams and storage weirs and their trajectories. This suggests that for complex data-scarce River systems, hybrid conceptual-fuzzy-logic modelling may be used for more detailed and dependable operational and planning analysis than stand-alone fuzzy modelling. Further work will include developing and testing other hybrid model configurations.
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
Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao
2014-09-18
A large challenge in the post-genomic era is to obtain the quantitatively dynamic interactive information of the important constitutes of underlying systems. The S-system is a dynamic and structurally rich model that determines the net strength of interactions between genes and/or proteins. Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models. Various evolutionary computation technologies have recently been developed for the identification of system parameters and skeletal-network structures. However, the gaps between the truncated and preserved terms remain too small. Additionally, current research methods fail to identify the structures of high dimensional systems (e.g., 30 genes with 1800 connections). Optimization technologies should converge fast and have the ability to adaptively adjust the search. In this study, we propose a seeding-inspired chemotaxis genetic algorithm (SCGA) that can force evolution to adjust the population movement to identify a favorable location. The seeding-inspired training strategy is a method to achieve optimal results with limited resources. SCGA introduces seeding-inspired genetic operations to allow a population to possess competitive power (exploitation and exploration) and a winner-chemotaxis-induced population migration to force a population to repeatedly tumble away from an attractor and swim toward another attractor. SCGA was tested on several canonical biological systems. SCGA not only learned the correct structure within only one to three pruning steps but also ensures pruning safety. The values of the truncated terms were all smaller than 10(-14), even for a thirty-gene system. PMID:25462336
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
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
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; 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
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
RULE-BASED INFERENCE SYSTEM FOR PREDICTING LINER/WASTE COMPATIBILITY
Determining the chemical compatibility of a liner material for containment of wastes rests mainly on the application of expert opinion to interpret the results of short-term immersion tests. A methodology known as a production system is employed to encode such expert opinion into...
Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H
2014-11-01
Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design. PMID:24953199
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
NASA Astrophysics Data System (ADS)
d'Onofrio, Alberto
2005-09-01
In this work we propose and investigate a family of models, which admits as particular cases some well known mathematical models of tumor-immune system interaction, with the additional assumption that the influx of immune system cells may be a function of the number of cancer cells. Constant, periodic and impulsive therapies (as well as the non-perturbed system) are investigated both analytically for the general family and, by using the model by Kuznetsov et al. [V.A. Kuznetsov, I.A. Makalkin, M.A. Taylor, A.S. Perelson, Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis, Bull. Math. Biol. (1994) 56(2) 295-321), via numerical simulations. Simulations seem to show that the shape of the function modeling the therapy is a crucial factor only for very high values of the therapy period T, whereas for realistic values of T, the eradication of the cancer cells depends on the mean values of the therapy term. Finally, some medical inferences are proposed.
NASA Astrophysics Data System (ADS)
Bearden, Kathryn
Interpenetrating polymer networks (IPNs), where polymer chains mechanically entangle during network formation, are of interest for their unique properties. The reaction sequence of a DGEBF epoxy/polybutadiene-dimethacrylate simultaneous IPN system was varied with differing catalysts to observe the correlation between reaction steps and physical properties. When the acrylate components were reacted first an IPN with two glass transitions and discrete phase separation was observed via scanning electron microscopy (SEM). When all components were reacted in parallel, two glass transitions were also observed but the morphology presented a single phase or a visible macro phase seperation. The IPN showed an increase in fracture toughness but a decrease in tensile strength compared to the single phase system and an epoxy control. Varying the amounts of polybutadiene-dimethacrylate in relation to the epoxy also showed a limit to the toughening effect.
NASA Astrophysics Data System (ADS)
Zlotnicki, Jacques; Toutain, Jean Paul; Sasai, Yoichi; Villacorte, Egardo; Bernard, Alain; Fauquet, Frederic; Nagao, Toshiyatsu
2010-05-01
On volcanoes which display hydrothermal/magmatic unrests, Electromagnetic (EM) methods can be combined with geochemical (GC) and thermal methods. The integration of these methods allows to image in detail hydrothermal systems, to find out possible scenarios of volcanic unrest, and to monitor the on-going activity with knowledge on the sources of heat, gas and fluid transfers. Since the 1990's the volcano shows recurrent periods of seismic activity, ground deformation, hydrothermal activity, and surface activity (geysers). Combined EM and GC methods noticeably contribute to map in detail the hydrothermal system and to analyse the sources of the activity: - Total magnetic field mapping evidences demagnetised zones over the two main areas forming the hydrothermal system (in the northern part of Main crater (MC)). These low magnetized areas are ascribed to thermal sources located at some hundreds metres of depth, - Self-potential surveys, delineate the contours of the fluids-heat transfer, and the northern and southern structural discontinuities enclosing the hydrothermal system, - Ground temperature gradient measurements evidence the distinctive heat transfer modes, from low fluxes related to soil temperature dominated by solar input to extremely high temperature gradients of 1200 °C m-1 or to more related to magmatic fluids. - Ground temperature and surface temperature of central acidic lake calculated by Thermal Aster imaging highlight the location of the most active ground fissures, outcrops and diffuse areas. Higher and larger anomalies are observed in the northern part of MC. A rough estimation of the thermal discharge in the northern part of the volcano gives 17 MW. - CO2 concentrations and fluxes from soil supply inform on fluids origin and on local processes operating along active fractures. Much higher carbon dioxide fluxes at MC sites confirm that the source of Taal activity is presently located in the northern part of the crater. - Heat and fluids release
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.
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.
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
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
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
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.
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
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
Nyamulagira’s magma plumbing system inferred from 15 years of InSAR
Wauthier, Christelle; Cayol, Valerie; Poland, Michael; Kervyn, François; D'Oreye, Nicolas; Hooper, Andrew; Samsonov, Sergei; Tiampo, Kristy; Smets, Benoit
2013-01-01
Nyamulagira, located in the east of the Democratic Republic of Congo on the western branch of the East African rift, is Africa’s most active volcano, with an average of one eruption every 3 years since 1938. Owing to the socio-economical context of that region, the volcano lacks ground-based geodetic measurements but has been monitored by interferometric synthetic aperture radar (InSAR) since 1996. A combination of 3D Mixed Boundary Element Method and inverse modelling, taking into account topography and source interactions, is used to interpret InSAR ground displacements associated with eruptive activity in 1996, 2002, 2004, 2006 and 2010. These eruptions can be fitted by models incorporating dyke intrusions, and some (namely the 2006 and 2010 eruptions) require a magma reservoir beneath the summit caldera. We investigate inter-eruptive deformation with a multi-temporal InSAR approach. We propose the following magma plumbing system at Nyamulagira by integrating numerical deformation models with other available data: a deep reservoir (c. 25 km depth) feeds a shallower reservoir (c. 4 km depth); proximal eruptions are fed from the shallow reservoir through dykes while distal eruptions can be fed directly from the deep reservoir. A dyke-like conduit is also present beneath the upper southeastern flank of Nyamulagira.
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
Schiller, Martin; Connelly, James N.; Glad, Aslaug C.; Mikouchi, Takashi; Bizzarro, Martin
2016-01-01
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. PMID:27429474
Amiri, Mohammad J; Abedi-Koupai, Jahangir; Eslamian, Sayed S; Mousavi, Sayed F; Hasheminejad, Hasti
2013-01-01
To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE. PMID:23383640
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.
Impulsive Noise Suppression from Images by Using Anfis Interpolant and Lillietest
NASA Astrophysics Data System (ADS)
Beşdok, Erkan
2004-12-01
A new impulsive noise (IN) elimination filter, entitled adaptive neuro-fuzzy inference system-based IN removal filter ( Anfis- F), which shows high performance at the restoration of images distorted by IN, is proposed in this paper. The Anfis- F comprises three main steps: finding the pixels that are suspected to be corrupted, the Delaunay triangulation, and finally, making estimation for intensity values of corrupted pixels within each of the Delaunay triangles. Extensive simulation results show that the proposed filter achieves better performance than other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.
Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers
NASA Astrophysics Data System (ADS)
Chang, C. K.; Azamathulla, H. Md; Zakaria, N. A.; Ghani, A. Ab
2012-02-01
This paper evaluates the performance of three soft computing techniques, namely Gene-Expression Programming (GEP) (Zakaria et al 2010), Feed Forward Neural Networks (FFNN) (Ab Ghani et al 2011), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the prediction of total bed material load for three Malaysian rivers namely Kurau, Langat and Muda. The results of present study are very promising: FFNN ( R 2 = 0.958, RMSE = 0.0698), ANFIS ( R 2 = 0.648, RMSE = 6.654), and GEP ( R 2 = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.
Ganesan, S; Victoire, T Aruldoss Albert; Vijayalakshmy, G
2014-01-01
In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal. PMID:24589837
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.
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
NASA Astrophysics Data System (ADS)
Valentine, Greg A.; Keating, Gordon N.
2007-09-01
We describe two small scoria cone volcanoes, Hidden Cone and Little Black Peak (ages between ~320-390 ka), in the Southwestern Nevada Volcanic Field and discuss their eruption mechanisms and inferences about their plumbing systems. Cone-forming pyroclastic deposits are consistent with eruptive styles ranging from Strombolian to violent Strombolian, and lavas emanated from near the bases of the cones. The volcanoes are monogenetic (rather than polycyclic, as allowed by previous geomorphic interpretations). Vents at each volcano appear to coincide with pre-existing normal faults, consistent with observations at older, deeply eroded volcanoes in the region. The existence of these two volcanoes on a topographically high area (particularly Hidden Cone) provides evidence for short feeder dike lengths (~500 m at the surface). We infer that this short length reflects the small length scale of the mantle source region that was tapped to feed each volcano.
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.
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…
Artificial Intelligence Techniques for the Estimation of Direct Methanol Fuel Cell Performance
NASA Astrophysics Data System (ADS)
Hasiloglu, Abdulsamet; Aras, Ömür; Bayramoglu, Mahmut
2016-04-01
Artificial neural networks and neuro-fuzzy inference systems are well known artificial intelligence techniques used for black-box modelling of complex systems. In this study, Feed-forward artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used for modelling the performance of direct methanol fuel cell (DMFC). Current density (I), fuel cell temperature (T), methanol concentration (C), liquid flow-rate (q) and air flow-rate (Q) are selected as input variables to predict the cell voltage. Polarization curves are obtained for 35 different operating conditions according to a statistically designed experimental plan. In modelling study, various subsets of input variables and various types of membership function are considered. A feed -forward architecture with one hidden layer is used in ANN modelling. The optimum performance is obtained with the input set (I, T, C, q) using twelve hidden neurons and sigmoidal activation function. On the other hand, first order Sugeno inference system is applied in ANFIS modelling and the optimum performance is obtained with the input set (I, T, C, q) using sixteen fuzzy rules and triangular membership function. The test results show that ANN model estimates the polarization curve of DMFC more accurately than ANFIS model.
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
NASA Astrophysics Data System (ADS)
Wolpert, David H.
2008-07-01
We show that physical devices that perform observation, prediction, or recollection share an underlying mathematical structure. We call devices with that structure “inference devices”. We present a set of existence and impossibility results concerning inference devices. These results hold independent of the precise physical laws governing our universe. In a limited sense, 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, these impossibility results can be viewed as a non-quantum-mechanical “uncertainty principle”. The mathematics of inference devices has close connections to the mathematics of Turing Machines (TMs). In particular, the impossibility results for inference devices are similar to the Halting theorem for TMs. Furthermore, one can define an analog of Universal TMs (UTMs) for inference devices. We call those analogs “strong inference devices”. We use strong inference devices to define the “inference complexity” of an inference task, which is the analog of the Kolmogorov complexity of computing a string. A task-independent bound is derived on how much the inference complexity of an inference task can differ for two different inference devices. This is analogous to the “encoding” bound governing how much the Kolmogorov complexity of a string can differ between two UTMs used to compute that string. However no universe can contain more than one strong inference device. So whereas the Kolmogorov complexity of a string is arbitrary up to specification of the UTM, there is no such arbitrariness in the inference complexity of an inference task. We informally discuss the philosophical implications of these results, e.g., for whether the universe “is” a computer. We also derive some graph-theoretic properties governing any set of multiple inference devices. We also present an
Eight challenges in phylodynamic inference
Frost, Simon D.W.; Pybus, Oliver G.; Gog, Julia R.; Viboud, Cecile; Bonhoeffer, Sebastian; Bedford, Trevor
2015-01-01
The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data. PMID:25843391
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)
Parisi, Serena; Paternoster, Michele; Kohfahl, Claus; Pekdeger, Asaf; Meyer, Hanno; Hubberten, Hans Wolfgang; Spilotro, Giuseppe; Mongelli, Giovanni
2011-02-01
Environmental isotope techniques, hydrogeochemical analysis and hydraulic data are employed to identify the main recharge areas of the Mt. Vulture hydrogeological basin, one of the most important aquifers of southern Italy. The groundwaters are derived from seepage of rainwater, flowing from the highest to the lowest elevations through the shallow volcanic weathered host-rock fracture zones. Samples of shallow and deep groundwater were collected at 48 locations with elevations ranging from 352 to 1,100 m above sea level (a.s.l.), for stable isotope (δ18O, δD) and major ion analyses. A complete dataset of available hydraulic information has been integrated with measurements carried out in the present study. Inferred recharge elevations, estimated on the basis of the local vertical isotopic gradient of δ18O, range between 550 and 1,200 m a.s.l. The isotope pattern of the Quaternary aquifer reflects the spatial separation of different recharge sources. Knowledge of the local hydrogeological setting was the starting point for a detailed hydrogeochemical and isotopic study to define the recharge and discharge patterns identifying the groundwater flow pathways of the Mt. Vulture basin. The integration of all the data allowed for the tracing of the groundwater flows of the Mt. Vulture basin.
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.
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.
A neuro-fuzzy controller for mobile robot navigation and multirobot convoying.
Ng, K C; Trivedi, M M
1998-01-01
A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets. PMID:18256001
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…
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.
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.
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.
HAMEDIAN, Amir Abbas; JAVID, Allahbakhsh; MOTESADDI ZARANDI, Saeed; RASHIDI, Yousef; MAJLESI, Monireh
2016-01-01
Background: Since the industrial revolution, the rate of industrialization and urbanization has increased dramatically. Regarding this issue, specific regions mostly located in developing countries have been confronted with serious problems, particularly environmental problems among which air pollution is of high importance. Methods: Eleven parameters, including CO, SO2, PM10, PM2.5, O3, NO2, benzene, toluene, ethyl-benzene, xylene, and 1,3-butadiene, have been accounted over a period of two years (2011–2012) from five monitoring stations located at Tehran, Iran, were assessed by using fuzzy inference system and fuzzy c-mean clustering. Results: These tools showed that the quality of criteria pollutants between the year 2011 and 2012 did not as much effect the public health as the other pollutants did. Conclusion: Using the air EPA AQI, the quality of air, and also the managerial plans required to improve the quality can be misled. PMID:27516999
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
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.
Koonin, Eugene V
2006-01-01
Background The core enzymes of the DNA replication systems show striking diversity among cellular life forms and more so among viruses. In particular, and counter-intuitively, given the central role of DNA in all cells and the mechanistic uniformity of replication, the core enzymes of the replication systems of bacteria and archaea (as well as eukaryotes) are unrelated or extremely distantly related. Viruses and plasmids, in addition, possess at least two unique DNA replication systems, namely, the protein-primed and rolling circle modalities of replication. This unexpected diversity makes the origin and evolution of DNA replication systems a particularly challenging and intriguing problem in evolutionary biology. Results I propose a specific succession for the emergence of different DNA replication systems, drawing argument from the differences in their representation among viruses and other selfish replicating elements. In a striking pattern, the DNA replication systems of viruses infecting bacteria and eukaryotes are dominated by the archaeal-type B-family DNA polymerase (PolB) whereas the bacterial replicative DNA polymerase (PolC) is present only in a handful of bacteriophage genomes. There is no apparent mechanistic impediment to the involvement of the bacterial-type replication machinery in viral DNA replication. Therefore, I hypothesize that the observed, markedly unequal distribution of the replicative DNA polymerases among the known cellular and viral replication systems has a historical explanation. I propose that, among the two types of DNA replication machineries that are found in extant life forms, the archaeal-type, PolB-based system evolved first and had already given rise to a variety of diverse viruses and other selfish elements before the advent of the bacterial, PolC-based machinery. Conceivably, at that stage of evolution, the niches for DNA-viral reproduction have been already filled with viruses replicating with the help of the archaeal
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 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.
Boutalis, Yiannis; Theodoridis, Dimitris C; Christodoulou, Manolis A
2009-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation. PMID:19273046
FUNCTIONAL OVERLAP OF ROOT SYSTEMS IN AN OLD-GROWTH FOREST INFERRED FROM TRACER 15N UPTAKE
Belowground competition for nutrients and water is considered a key factor affecting spatial organization and productivity of individual stems within forest stands, yet there are few data describing the lateral extent and overlap of competing root systems. We quantified the func...
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
Navajas-Pérez, Rafael; de la Herrán, Roberto; López González, Ginés; Jamilena, Manuel; Lozano, Rafael; Ruiz Rejón, Carmelo; Ruiz Rejón, Manuel; Garrido-Ramos, Manuel A
2005-09-01
The genus Rumex includes hermaphroditic, polygamous, gynodioecious, monoecious, and dioecious species, with the dioecious species being represented by different sex-determining mechanisms and sex-chromosome systems. Therefore, this genus represents an exceptional case study to test several hypotheses concerning the evolution of both mating systems and the genetic control of sex determination in plants. Here, we compare nuclear intergenic transcribed spacers and chloroplast intergenic sequences of 31 species of Rumex. Our phylogenetic analysis supports a systematic classification of the genus, which differs from that currently accepted. In contrast to the current view, this new phylogeny suggests a common origin for all Eurasian and American dioecious species of Rumex, with gynodioecy as an intermediate state on the way to dioecy. Our results support the contention that sex determination based on the balance between the number of X chromosomes and the number of autosomes (X/A balance) has evolved secondarily from male-determining Y mechanisms and that multiple sex-chromosome systems, XX/XY1Y2, were derived twice from an XX/XY system. The resulting phylogeny is consistent with a classification of Rumex species according to their basic chromosome number, implying that the evolution of Rumex species might have followed a process of chromosomal reduction from x = 10 toward x = 7 through intermediate stages (x = 9 and x = 8). PMID:15944442
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.
NASA Astrophysics Data System (ADS)
Birhanu, Yelebe; Biggs, Juliet; Gottsmann, Joachim; Lewi, Elias; Lloyd, Ryan; Bekele, Berhanu
2016-04-01
Silicic volcanic centres in the rift systems frequently experience unrest indicating long-term activity in the underlying magmatic system, but it is difficult to distinguish the contributions of hydrothermal fluids, magma or gasses. Aluto volcano which is located in the central MER system is situated between the Lakes Ziway and Langano in the north and south respectively. Continuous GPS installed from April 2013 to October 2015 shows subsidence initially, with the largest subsidence observed in the eastern part of the caldera (2 cm/yr). InSAR observations from TerraSAR-X show a radially-symmetric pattern of long-term subsidence. Dynamic gravity surveys carried out in October 2014 and 2015 showed that there is a net mass loss in the western and central part of the caldera and mass gain in the eastern and southern part of the caldera, with a sharp gradient between the two. This complex spatial pattern of gravity change is significantly different to the simple pattern of deformation indicating multiple sources of pressure and mass change exist within the caldera. We explain the ratio of gravity to height change (dg/dh) throughout the volcano by considering cooling and crystallisation of magma body, draining and precipitation of hydrothermal fluids and changes in the water table and lake levels. Keywords: volcano deformation, dynamic gravity, continental rift
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
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.
"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
Hanson, K.M.; Cunningham, G.S.
1996-04-01
The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.
Griscom, A.; Jachens, R.C.
1989-01-01
Geologic and geophysical data for the San Andreas fault system north of San Francisco suggest that the eastern boundary of the Pacific plate migrated eastward from its presumed original position at the base of the continental slope to its present position along the San Andreas transform fault by means of a series of eastward jumps of the Mendocino triple junction. These eastward jumps total a distance of about 150 km since 29 Ma. Correlation of right-laterally displaced gravity and magnetic anomalies that now have components at San Francisco and on the shelf north of Point Arena indicates that the presently active strand of the San Andreas fault north of the San Francisco peninsula formed recently at about 5 Ma when the triple junction jumped eastward a minimum of 100 km to its present location at the north end of the San Andreas fault. -from Authors
Inferring genetic networks from microarray data.
May, Elebeoba Eni; Davidson, George S.; Martin, Shawn Bryan; Werner-Washburne, Margaret C.; Faulon, Jean-Loup Michel
2004-06-01
In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.
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
Probabilistic inferences related to the measurement process
NASA Astrophysics Data System (ADS)
Rossi, G. B.
2010-07-01
In measurement indications from a measuring system are acquired and, on the basis of them, some inference about the measurand is made. The final result may be the assignment of a probability distribution for the possible values of the measurand. We discuss the logical structure of such an inference and some of its epistemological consequences. In particular, we propose a new solution to the problem of systematic effects in measurement.
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
Hurwitz, S.; Lowenstern, J. B.; Heasler, H.
2007-01-01
We present and analyze a chemical dataset that includes the concentrations and fluxes of HCO3-, SO42-, Cl-, and F- in the major rivers draining Yellowstone National Park (YNP) for the 2002-2004 water years (1 October 2001 - 30 September 2004). The total (molar) flux in all rivers decreases in the following order, HCO3- > Cl- > SO42- > F-, but each river is characterized by a distinct chemical composition, implying large-scale spatial heterogeneity in the inputs of the various solutes. The data also display non-uniform temporal trends; whereas solute concentrations and fluxes are nearly constant during base-flow conditions, concentrations decrease, solute fluxes increase, and HCO3-/Cl-, and SO42-/Cl- increase during the late-spring high-flow period. HCO3-/SO42- decreases with increasing discharge in the Madison and Falls Rivers, but increases with discharge in the Yellowstone and Snake Rivers. The non-linear relations between solute concentrations and river discharge and the change in anion ratios associated with spring runoff are explained by mixing between two components: (1) a component that is discharged during base-flow conditions and (2) a component associated with snow-melt runoff characterized by higher HCO3-/Cl- and SO42-/Cl-. The fraction of the second component is greater in the Yellowstone and Snake Rivers, which host lakes in their drainage basins and where a large fraction of the solute flux follows thaw of ice cover in the spring months. Although the total river HCO3- flux is larger than the flux of other solutes (HCO3-/Cl- ??? 3), the CO2 equivalent flux is only ??? 1% of the estimated emission of magmatic CO2 soil emissions from Yellowstone. No anomalous solute flux in response to perturbations in the hydrothermal system was observed, possibly because gage locations are too distant from areas of disturbance, or because of the relatively low sampling frequency. In order to detect changes in river hydrothermal solute fluxes, sampling at higher
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.
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.
Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach
NASA Astrophysics Data System (ADS)
Taufik, Afirah; Sakinah Syed Ahmad, Sharifah
2016-06-01
The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.
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
Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach
NASA Astrophysics Data System (ADS)
Khandait, P. D.; Bawane, N. G.; Limaye, S. S.
2010-02-01
This paper deals with improved ECG signal analysis using Wavelet Transform Techniques and employing subsequent modified feature extraction for Arrhythmia detection based on Neuro-Fuzzy technique. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia . Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia and CSE databases, developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals are used as inputs to the classifiers. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95.13% is achieved which is a significant improvement.
NASA Astrophysics Data System (ADS)
Relvas, P.; Luis, J. M.; Silva, P. L.; Santos, A. M.
2011-12-01
Satellite-derived sea surface temperature (SST) trends are built at the pixel scale to investigate long term changes in oceanic patterns. We consider that the SST time-series already available is long enough to attempt the analysis at the decadal scale. The analysis extends from 1982 to 2009 and is applied to the eastern boundary of the North Atlantic, from 10 to 45 N extending until 30 W, covering the Canary Current Upwelling System. Monthly mean SST data from the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA series satellites, with a spatial resolution of 4x4 km, were provided by the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory. SST estimates are derived from the Pathfinder Version 5 algorithms. Whenever possible the time series are limited to the night-time passes to avoid any solar heating effect. Only high quality SST values with a flag assignment of 6 and 7 (Kilpatrick et al., 2001) are used. Using only the highest quality values creates a data processing problem that is posed by the voids left on the temperature grids on the node positions corresponding to the rejected values. The case is further complicated by the fact that the voids, originated by unfavourable weather conditions of strong cloud cover and coastal fogs, most intense during strong upwelling events, are located on variable positions on the grids depending on the month the grid refers to. This is particular evident on the winter months where large areas of the ocean did not have any reliable measure, even on a monthly average. We apply several procedures to fill these data gaps that guarantee that annual and seasonal averages are not biased towards summer temperatures. To investigate the spatial variability of the long term SST trend a robust linear fit was applied to each individual pixel, crossing along the time the same 4x4 km pixel in all the processed monthly mean AVHRR SST images from 1982 until 2009. Fields of
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
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.
Warburton, Elizabeth M; Kam, Michael; Bar-Shira, Enav; Friedman, Aharon; Khokhlova, Irina S; Koren, Lee; Asfur, Mustafa; Geffen, Eli; Kiefer, Daniel; Krasnov, Boris R; Degen, A Allan
2016-09-01
Evaluating host resistance via parasite fitness helps place host-parasite relationships within evolutionary and ecological contexts; however, few studies consider both these processes simultaneously. We investigated how different levels of parasite pressure affect parasite mortality and reproductive success in relationship to host defense efforts, using the rodent Gerbillus nanus and the flea Xenopsylla conformis as a host-parasite system. Fifteen immune-naïve male rodents were infested with 20, 50, or 100 fleas for four weeks. During this time number of new imagoes produced per adult flea (our flea reproductive output metric), flea mortality, and change in circulating anti-flea immunoglobulin G (our measure of adaptive immune defense) were monitored. Three hypotheses guided this work: (1) increasing parasite pressure would heighten host defenses; (2) parasite mortality would increase and parasite reproductive output would decrease with increasing investment in host defense; and (3) hosts under high parasite pressure could invest in behavioral and/or immune responses. We predicted that at high infestation levels (a) parasite mortality would increase; (b) flea reproductive output per individual would decrease; and (c) host circulating anti-flea antibody levels would increase. The hypotheses were partially supported. Flea mortality significantly increased and flea reproductive output significantly decreased as flea pressure increased. Host adaptive immune defense did not significantly change with increasing flea pressure. Therefore, we inferred that investment in host behavioral defense, either alone or in combination with density-dependent effects, may be more efficient at increasing flea mortality and decreasing flea reproductive output than antibody production during initial infestation in this system. PMID:27130319
NASA Astrophysics Data System (ADS)
Mirmomeni, Masoud; Shafiee, Masoud; Lucas, Caro; Araabi, Babak Nadjar
2006-12-01
In the last two decades, researches indicate that the physical precursor and solar dynamo techniques are preferred as practical tools for long term prediction of solar activity. But, why more than 23 cycles of solar activity history should be omitted and just use the empirical methods or simple autoregressive methods on the basis of observations for the latest eight cycles? In this article, a method based on fuzzy descriptor systems (as a generalization of ordinary Takagi Sugeno (T S) neuro-fuzzy models), developed by the authors to yield a satisfactory solution to the unresolved problem of nonlinear descriptor system identification, and singular spectrum analysis (SSA) as one of the spectral analysis is proposed to forecast some of solar activity's indexes in the way that, a fuzzy descriptor model is optimized for each of the principal components obtained from SSA, and the multi step predicted values are recombined to make the disturbance storm time (DST) and proton flux indexes. The proposed method is used for forecasting hourly DST index in 2001 and daily average of the DST index from 1957 to 2005 and proton flux index in 2001. The results are remarkably good in the predictions of DST and proton flux indexes.
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.
Active inference and learning.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O'Doherty, John; Pezzulo, Giovanni
2016-09-01
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. PMID:27375276
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.
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.
Towards Context Sensitive Information Inference.
ERIC Educational Resources Information Center
Song, D.; Bruza, P. D.
2003-01-01
Discusses information inference from a psychologistic stance and proposes an information inference mechanism that makes inferences via computations of information flow through an approximation of a conceptual space. Highlights include cognitive economics of information processing; context sensitivity; and query models for information retrieval.…
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.
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.
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…
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.
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
Moment inference from tomograms
Day-Lewis, F. D.; Chen, Y.; Singha, K.
2007-01-01
Time-lapse geophysical tomography can provide valuable qualitative insights into hydrologic transport phenomena associated with aquifer dynamics, tracer experiments, and engineered remediation. Increasingly, tomograms are used to infer the spatial and/or temporal moments of solute plumes; these moments provide quantitative information about transport processes (e.g., advection, dispersion, and rate-limited mass transfer) and controlling parameters (e.g., permeability, dispersivity, and rate coefficients). The reliability of moments calculated from tomograms is, however, poorly understood because classic approaches to image appraisal (e.g., the model resolution matrix) are not directly applicable to moment inference. Here, we present a semi-analytical approach to construct a moment resolution matrix based on (1) the classic model resolution matrix and (2) image reconstruction from orthogonal moments. Numerical results for radar and electrical-resistivity imaging of solute plumes demonstrate that moment values calculated from tomograms depend strongly on plume location within the tomogram, survey geometry, regularization criteria, and measurement error. Copyright 2007 by the American Geophysical Union.
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.
Reliability of the Granger causality inference
NASA Astrophysics Data System (ADS)
Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David
2014-04-01
How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.
Gene-network inference by message passing
NASA Astrophysics Data System (ADS)
Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.
2008-01-01
The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.
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.
Bayesian inference in geomagnetism
NASA Technical Reports Server (NTRS)
Backus, George E.
1988-01-01
The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.
An evaluation of explanations of probabilistic inference.
Suermondt, H. J.; Cooper, G. F.
1992-01-01
Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results, to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear. PMID:1482939
Dynamical inference of hidden biological populations
NASA Astrophysics Data System (ADS)
Luchinsky, D. G.; Smelyanskiy, V. N.; Millonas, M.; McClintock, P. V. E.
2008-10-01
Population fluctuations in a predator-prey system are analyzed for the case where the number of prey could be determined, subject to measurement noise, but the number of predators was unknown. The problem of how to infer the unmeasured predator dynamics, as well as the model parameters, is addressed. Two solutions are suggested. In the first of these, measurement noise and the dynamical noise in the equation for predator population are neglected; the problem is reduced to a one-dimensional case, and a Bayesian dynamical inference algorithm is employed to reconstruct the model parameters. In the second solution a full-scale Markov Chain Monte Carlo simulation is used to infer both the unknown predator trajectory, and also the model parameters, using the one-dimensional solution as an initial guess.
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.
DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.
Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond
2016-01-01
To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312
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.
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…
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.…
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…
Learning to Observe "and" Infer
ERIC Educational Resources Information Center
Hanuscin, Deborah L.; Park Rogers, Meredith A.
2008-01-01
Researchers describe the need for students to have multiple opportunities and social interaction to learn about the differences between observation and inference and their role in developing scientific explanations (Harlen 2001; Simpson 2000). Helping children develop their skills of observation and inference in science while emphasizing the…
INFERRING THE ECCENTRICITY DISTRIBUTION
Hogg, David W.; Bovy, Jo; Myers, Adam D.
2010-12-20
Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision-other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts-so long as the measurements have been communicated as a likelihood function or a posterior sampling.
Inferring the Eccentricity Distribution
NASA Astrophysics Data System (ADS)
Hogg, David W.; Myers, Adam D.; Bovy, Jo
2010-12-01
Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision—other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts—so long as the measurements have been communicated as a likelihood function or a posterior sampling.
Inference from aging information.
de Oliveira, Evaldo Araujo; Caticha, Nestor
2010-06-01
For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps. PMID:20421181
ERIC Educational Resources Information Center
McConnell, John W.; Bowers, Norman D.
Data derived from a year-long study of 43 algebra classes in 13 high schools were collected to assess the differences between high-inference and low-inference measures of teacher behavior as predictors of pupil achievement and attitude change. The high-inference systems rated characteristics such as enthusiasm and clarity while low-inference…
A wavelet transform based feature extraction and classification of cardiac disorder.
Sumathi, S; Beaulah, H Lilly; Vanithamani, R
2014-09-01
This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652
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.
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.
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.
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
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.
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
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
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.
Linear and nonlinear quantitative structure-property relationship modelling of skin permeability.
Khajeh, A; Modarress, H
2014-01-01
In this work, quantitative structure-property relationship (QSPR) models were developed to estimate skin permeability based on theoretically derived molecular descriptors and a diverse set of experimental data. The newly developed method combining modified particle swarm optimization (MPSO) and multiple linear regression (MLR) was used to select important descriptors and develop the linear model using a training set of 225 compounds. The adaptive neuro-fuzzy inference system (ANFIS) was used as an efficient nonlinear method to correlate the selected descriptors with experimental skin permeability data (log Kp). The linear and nonlinear models were assessed by internal and external validation. The obtained models with three descriptors show good predictive ability for the test set, with coefficients of determination for the MPSO-MLR and ANFIS models equal to 0.874 and 0.890, respectively. The QSPR study suggests that hydrophobicity (encoded as log P) is the most important factor in transdermal penetration. PMID:24090175
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
Computing resonant frequency of C-shaped compact microstrip antennas by using ANFIS
NASA Astrophysics Data System (ADS)
Akdagli, Ali; Kayabasi, Ahmet; Develi, Ibrahim
2015-03-01
In this work, the resonant frequency of C-shaped compact microstrip antennas (CCMAs) operating at UHF band is computed by using the adaptive neuro-fuzzy inference system (ANFIS). For this purpose, 144 CCMAs with various relative dielectric constants and different physical dimensions were simulated by the XFDTD software package based on the finite-difference time domain (FDTD) method. One hundred and twenty-nine CCMAs were employed for training, while the remaining 15 CCMAs were used for testing of the ANFIS model. Average percentage error (APE) values were obtained as 0.8413% and 1.259% for training and testing, respectively. In order to demonstrate its validity and accuracy, the proposed ANFIS model was also tested over the simulation data given in the literature, and APE was obtained as 0.916%. These results show that ANFIS can be successfully used to compute the resonant frequency of CCMAs.
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.
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.
Hydrograph estimation with fuzzy chain model
NASA Astrophysics Data System (ADS)
Güçlü, Yavuz Selim; Şen, Zekai
2016-07-01
Hydrograph peak discharge estimation is gaining more significance with unprecedented urbanization developments. Most of the existing models do not yield reliable peak discharge estimations for small basins although they provide acceptable results for medium and large ones. In this study, fuzzy chain model (FCM) is suggested by considering the necessary adjustments based on some measurements over a small basin, Ayamama basin, within Istanbul City, Turkey. FCM is based on Mamdani and the Adaptive Neuro Fuzzy Inference Systems (ANFIS) methodologies, which yield peak discharge estimation. The suggested model is compared with two well-known approaches, namely, Soil Conservation Service (SCS)-Snyder and SCS-Clark methodologies. In all the methods, the hydrographs are obtained through the use of dimensionless unit hydrograph concept. After the necessary modeling, computation, verification and adaptation stages comparatively better hydrographs are obtained by FCM. The mean square error for the FCM is many folds smaller than the other methodologies, which proves outperformance of the suggested methodology.
NASA Astrophysics Data System (ADS)
Saeed, R. A.; Galybin, A. N.; Popov, V.
2013-01-01
This paper discusses condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.
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.
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
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.
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.
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
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
Causal inference and developmental psychology.
Foster, E Michael
2010-11-01
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 the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference-tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of "ignorability." The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development. PMID:20677855
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
Inference in {open_quotes}poor{close_quotes} languages
Petrov, S.
1996-12-31
Languages with a solvable implication problem but without complete and consistent systems of inference rules ({open_quote}poor{close_quote} languages) are considered. The problem of existence of a finite, complete, and consistent inference rule system for a {open_quotes}poor{close_quotes} language is stated independently of the language or the rule syntax. Several properties of the problem are proved. An application of the results to the language of join dependencies is given.
NASA Astrophysics Data System (ADS)
Robion, Philippe; Mehl, Caroline
2016-04-01
We propose to investigate the set up mechanisms of sands injection in the case of dykes injected in host marls of Aptian-Albian age in the Vocontian basin (SE France). Several models have been proposed for a downward injection of the dyke in the Bevons area and we guess that AMS fabric investigations can be used to infer the flow direction. 144 drill cores distributed on 14 sites were sampled, among which 8 sites in the injectites and 6 sites in the host rocks. The studied dykes are generally of a few decimeters thick and are setting up in both in vertical or oblique position with respect to the subhorizontal bedding of the host rocks. There were sampled from one side to the other in order to track the flow direction by identification of imbricated fabric. Magnetic mineralogy, i.e. unblocking temperature inferred from IRM 3 axes demagnetization, indicates that the ferromagnetics s.l. mineralogy is dominated by an assemblage of magnetite (unblocking temperature Tub=580°C) and pyrrhotite (Tub=325°C). Magnetic susceptibility is low, typical for siliciclastic rocks, ranging from 4x10-5 up to 1.7x10-4 SI. Degree of magnetic anisotropy is likely representative of AMS measurements in sedimentary rocks with weak values, below than 5 %. In marly host rocks magnetic mineralogy is dominated by pyrrhotite associated with magnetite and both the magnetic susceptibility and degree of anisotropy are slightly lower than for injectites. Regarding magnetic fabric axes distribution, despite some dispersion, the results show that minimum axes of AMS (K3) are parallel to the dyke plane, and maximum axes (K1) are roughly in horizontal position. In marly host rocks, the magnetic fabric is related to tectonic shortening. We interpret that the host rocks have recorded the regional tectonic imprint while the magnetic fabric of the injectites are related to early sedimentary processes. The mechanism of set up proposed to explain the magnetic fabric in the Bevon injectites is a step
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
NASA Astrophysics Data System (ADS)
Toroczkai, Zoltan
Jaynes's maximum entropy method provides a family of principled models that allow the prediction of a system's properties as constrained by empirical data (observables). However, their use is often hindered by the degeneracy problem characterized by spontaneous symmetry breaking, where predictions fail. Here we show that degeneracy appears when the corresponding density of states function is not log-concave, which is typically the consequence of nonlinear relationships between the constraining observables. We illustrate this phenomenon on several examples, including from complex networks, combinatorics and classical spin systems (e.g., Blume-Emery-Griffiths lattice-spin models). Exploiting these nonlinear relationships we then propose a solution to the degeneracy problem for a large class of systems via transformations that render the density of states function log-concave. The effectiveness of the method is demonstrated on real-world network data. Finally, we discuss the implications of these findings on the relationship between the geometrical properties of the density of states function and phase transitions in spin systems. Supported in part by Grant No. FA9550-12-1-0405 from AFOSR/DARPA and by Grant No. HDTRA 1-09-1-0039 from DTRA.