Evapotranspiration estimation by two different neuro-fuzzy inference systems
NASA Astrophysics Data System (ADS)
Cobaner, Murat
2011-02-01
SummaryThe potential of two different adaptive network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems in modeling of reference evapotranspiration (ET 0) are investigated in this paper. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system, named G-ANFIS, and (2) subtractive clustering based fuzzy inference system, named S-ANFIS. In the first part of the study, the performance of resultant FIS was compared and the effect of parameters was investigated. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from Santa Monica, in Los Angeles, USA, are used as inputs to the FIS models so as to estimate ET 0 obtained using the FAO-56 Penman-Monteith equation. In the second part of the study, the estimates of the FIS models are compared with those of artificial neural network (ANN) approach, namely, multi-layer perceptron (MLP), and three empirical models, namely, CIMIS Penman, Hargreaves and Ritchie methods. Root mean square error, mean absolute error and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the S-ANFIS model yields plausible accuracy with fewer amounts of computations as compared to the G-ANFIS and MLP models in modeling the ET 0 process.
Suat Akbulut; A. Samet Hasiloglu; Sibel Pamukcu
2004-01-01
Neuro-fuzzy inference systems have been used in many areas in civil engineering applications. This study was conducted to estimate low strain dynamic properties of composite media from easily measurable physical properties using the adaptive neuro-fuzzy inference system (ANFIS). The inference system was employed to predict the shear modulus and the damping coefficient of the sand samples as an alternative to
Cheng-long Chuang; Chung-ming Chen; Grace S. Shieh; Joe-air Jiang
2007-01-01
A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the
Adaptive neuro fuzzy inference system for profiling of the atmosphere
NASA Astrophysics Data System (ADS)
Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.
2014-03-01
Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June-September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival
Aickelin, Uwe
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival Hazlina Hamdan for breast cancer. I. INTRODUCTION Breast cancer is one of the most common cancers to afflict the female population. It is estimated that one in nine women in the UK will develop breast cancer at some point
An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
Ulas Çaydas; Ahmet Hasçalik; Sami Ekici
2009-01-01
A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness
A. Besalatpour; M. A. Hajabbasi; S. Ayoubi; M. Afyuni; A. Jalalian; R. Schulin
2012-01-01
Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent
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
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
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
K. T. Chau; K. C. Wu; C. C. Chan
2004-01-01
This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged\\/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output,
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.
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, Gholam Abbas; Habibi-Nokhandan, Majid; Mousavi-Baygi, Mohammad; Khoshhal, Javad; Shaemi Barzoki, Akbar
2010-07-01
This paper aims to study the relationship between large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neuro-fuzzy inference system (ANFIS) was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and the 1,000-hPa level, the temperature of the 700-hPa level, the thickness between the 500- and 1,000-hPa levels, the relative humidity at the 300-hPa level, and precipitable water content. We have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the ANFIS in the period 1970-1997 has been taught. Finally, we forecast the rainfall for the period 1998-2007. The results show that the ANFIS can predict the rainfall with reasonable accuracy.
Modelling Dissolved Pollutants in Krishna River Using Adaptive Neuro Fuzzy Inference Systems
NASA Astrophysics Data System (ADS)
Matli, C. S.; Umamahesh, N. V.
2014-01-01
Water quality models are used to describe the discharge concentration relationships in the river. Number of models exists to simulate the pollutant loads in a river, of which some of them are based on simple cause effect relationships and others on highly sophisticated physical and mathematical approaches that require extensive data inputs. Fuzzy rule based modeling extensively used in other disciplines, is attempted in the present study for modeling water quality with respect of dissolved pollutants in Krishna river flowing in Southern part of India. Adaptive Neuro Fuzzy Inference Systems (ANFIS), a recent development in the area of neuro-computing, based on the concept of fuzzy sets is used to model highly non-linear relationships and are capable of adaptive learning. This paper presents the results of the application of ANFIS for modeling dissolved pollutants in the Krishna River. The application and validation of the models is carried out using water quality and flow data obtained from the monitoring stations on the river. The results indicate that the models are quite successful in simulating the physical processes of the relationships between discharge and concentrations.
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 ANFIS) has limited success.
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
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
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
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 (?) 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
Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems] [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering] [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control] [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)] [Electric Power Research Inst., Palo Alto, CA (United States)
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.
Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems
Samira Chabaa; Abdelouhab Zeroual
2009-01-01
Problem statement: The statistical modeling for predicting network tr affic has now become a major tool used for network and is of sign ificant interest in many domains: Adaptive application, congestion and admission control, wire less, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic
Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, Wen-Ming; Li, R K; Wang, Tzu-Hao
2012-04-01
Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively. PMID:20703710
NASA Astrophysics Data System (ADS)
Ajay Kumar, M.; Srikanth, N.
2014-03-01
In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.
Differentiating between good credits and bad credits using neuro-fuzzy systems
Rashmi Malhotra; D. K. Malhotra
2002-01-01
To evaluate consumer loan applications, loan officers use many techniques such as judgmental systems, statistical models, or simply intuitive experience. In recent years, fuzzy systems and neural networks have attracted the growing interest of researchers and practitioners. This study compares the performance of artificial neuro-fuzzy inference systems (ANFIS) and multiple discriminant analysis models to screen potential defaulters on consumer loans.
Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors
Alexandre Evsukoff; Sylviane Gentil
2005-01-01
This paper presents an application of recurrent neuro-fuzzy systems to fault detection and isolation in nuclear reactors. A general framework is adopted, in which a fuzzification module is linked to an inference module that is actually a neural network adapted to the recognition of the dynamic evolution of process variables and related faults. Process data is fuzzified in order to
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.
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.
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
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
Adaptive neuro-fuzzy control of dynamical systems
Alok Kanti Deb; Alok Juyal
2011-01-01
In this paper, the an adaptive neuro-fuzzy control that combines the features of fuzzy sets and neural networks have been implemented and applied for the control of SISO and MIMO systems. Duffing forced oscillation system was considered as the SISO plant while the Twin Rotor laboratory set up that closely mimics helicopter dynamics was considered as the MIMO plant. The
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
Arun Khosla; Shakti Kumar; K. K. Aggarwal
2003-01-01
ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates
M. B. Djukanovic; M. S. Calovic; B. V. Vesovic; D. J. Sobajic
1997-01-01
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,
Adaptive neuro-fuzzy estimation of optimal lens system parameters
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor; Pavlovi?, 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.
Adaptive Neuro-Fuzzy Intrusion Detection Systems
Sampada Chavan; Khusbu Shah; Neha Dave; Sanghamitra Mukherjee; Ajith Abraham; Sugata Sanyal
2004-01-01
The Intrusion Detection System architecture commonly used in commercial and research systems have a number of problems that limit their configurability, scalability or efficiency. In this paper, two machine-learning paradigms, Artificial Neural Networks and Fuzzy Inference System, are used to design an Intrusion Detection System. SNORT is used to perform real time traffic analysis and packet logging on IP network
José António Barros Vieira; Fernando Morgado Dias; Alexandre Manuel Mot
This article presents a comparison of Artificial Neural Networks and Neuro- Fuzzy Systems applied for modeling and controlling a real system. The main objective is to control the temperature inside of a ceramics kiln. The details of all system components are described. The steps taken to arrive at the direct and inverse models using the two architectures: Adaptive Neuro Fuzzy
Neuro-fuzzy modeling and control
JYH-SHING ROGER JANG; Chuen-Tsai Sun
1995-01-01
Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce
Lu-Hang Zong; Xing-Long Gong; Chao-Yang Guo; Shou-Hu Xuan
2011-01-01
In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc–Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on
Lu-Hang Zong; Xing-Long Gong; Chao-Yang Guo; Shou-Hu Xuan
2012-01-01
In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc–Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on
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.
A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments.
del Campo, Inés; Basterretxea, Koldo; Echanobe, Javier; Bosque, Guillermo; Doctor, Faiyaz
2012-04-01
This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines. PMID:22010155
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
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 hierarchical neuro-fuzzy system for identification of simultaneous faults in hydraulic servovalves
Haitham Rashidy; S. Rezeka; A. Saafan; Taher Awad
2003-01-01
This paper addresses the problem of identifying either single or simultaneous faults that may practically occur in hydraulic servovalves. A hierarchical neuro-fuzzy system is proposed to deal with the complex data and the interference between the phenomenological features of the faults. The proposed system decomposes the task of identification into three manageable subtasks. A mechanism of information abstraction from each
Forecasting of natural gas consumption with neural network and neuro fuzzy system
NASA Astrophysics Data System (ADS)
Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan
2010-05-01
The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting
Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm
Debrup Chakraborty; Nikhil R. Pal
2001-01-01
Abstract—Most methods of fuzzy rule-based system identifica- tion (SI) either ignore feature analysis or do it in a separate phase. This paper proposes a novel neuro-fuzzy system that can simulta- neously do feature analysis and SI in an integrated manner. It is a five-layered feed-forward network for realizing a fuzzy rule-based system. The second layer of the net is the
A Neuro-fuzzy Learning System for Adaptive Swarm Behaviors Dealing with Continuous State Space
Takashi Kuremoto; Masanao Obayashi; Kunikazu Kobayashi; Hirotaka Adachi; Kentaro Yoneda
2008-01-01
Swarm intelligence has brought a new paradise for function optimization, structural optimization, multi-agent systems and\\u000a other study fields. In our previous work, we proposed a neuro-fuzzy system using reinforcement learning algorithm (actor-critic\\u000a method with TD error learning algorithm) to acquire optimized swarm behaviors. This paper improves the conventional learning\\u000a system, which only deals with discrete state space and action space,
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor; Shamshirband, Shahaboddin; Pavlovi?, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlovi?, 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.
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
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
Victor Boskovitz; Hugo Guterman
2002-01-01
An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is
Inference of S-wave velocities from well logs using a Neuro-Fuzzy Logic (NFL) approach
NASA Astrophysics Data System (ADS)
Aldana, Milagrosa; Coronado, Ronal; Hurtado, Nuri
2010-05-01
The knowledge of S-wave velocity values is important for a complete characterization and understanding of reservoir rock properties. It could help in determining fracture propagation and also to improve porosity prediction (Cuddy and Glover, 2002). Nevertheless the acquisition of S-wave velocity data is rather expensive; hence, for most reservoirs usually this information is not available. In the present work we applied a hybrid system, that combines Neural Networks and Fuzzy Logic, in order to infer S-wave velocities from porosity (?), water saturation (Sw) and shale content (Vsh) logs. The Neuro-Fuzzy Logic (NFL) technique was tested in two wells from the Guafita oil field, Apure Basin, Venezuela. We have trained the system using 50% of the data randomly taken from one of the wells, in order to obtain the inference equations (Takani-Sugeno-Kang (TSK) fuzzy model). Equations using just one of the parameters as input (i.e. ??, Sw or Vsh), combined by pairs and all together were obtained. These equations were tested in the whole well. The results indicate that the best inference (correlation between inferred and experimental data close to 80%) is obtained when all the parameters are considered as input data. An increase of the equation number of the TSK model, when one or just two parameters are used, does not improve the performance of the NFL. The best set of equations was tested in a nearby well. The results suggest that the large difference in the petrophysical and lithological characteristics between these two wells, avoid a good inference of S-wave velocities in the tested well and allowed us to analyze the limitations of the method.
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
NEURO FUZZY SYSTEM FOR INDUSTRIAL PROCESSES FAULT DIAGNOSIS
Marius Pislaru; Alexandru Trandabat; Marius Olariu
In the last decade considerable research efforts have been spent to seek for systematic approaches to Fault Diagnosis (FD) in dynamical systems The problem of fault detection consists in detecting faults in a physical system by monitoring its inputs and outputs .Recently, the research has focused on non-linear systems FDI. Traditionally, the FD problem for non-linear dynamic systems has been
Some remarks on adaptive neuro-fuzzy systems
Romeo Ortega; Genie Informatique
1995-01-01
Makes three remarks concerning adaptive implementations of neural networks and fuzzy systems. First, the author brings to the readers attention the fact that the potential power of these systems as function approximators is lost when, as done in recently published work, the adjustable parameters are only the linear combination weights of the basis functions. Second, the author shows that the
Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy
Substation Final Project Report Power Systems Engineering Research Center A National Science Foundation Report Intelligent Substation Project Team Rahmat Shoureshi, Project Leader Tim Norick Ryan Administration and its employees for their support of this project. The use of their substation for testing
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
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
Marcos J. Ara; M. Cano-Izquierdoc; Yannis A. Dimitriadisd
This paper addresses the automatization of a penicillin production process with the development of soft sensors as well as Internal Model Controllers (IMC) for a penicillin fermentation plant using modules based on FasArt and FasBack neuro-fuzzy systems. While soft sensors are intended to aid the human supervision of the process currently being conducted at pilot plants, the proposed controller will
Marcos J. Araúzo-Bravo; José M. Cano-Izquierdo; Eduardo Gómez-Sánchez; Manuel J. López-Nieto; Yannis A. Dimitriadis; Juan López-Coronado
2004-01-01
This paper addresses the automatization of a penicillin production process with the development of soft sensors as well as Internal Model Controllers (IMC) for a penicillin fermentation plant using modules based on FasArt and FasBack neuro-fuzzy systems. While soft sensors are intended to aid the human supervision of the process currently being conducted at pilot plants, the proposed controller will
A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system
Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken [Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nakamachi (Japan)
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.
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
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method. PMID:23924413
A neuro-fuzzy control system for intelligent overlock sewing machines
George Stylios; J. O. Sotomi
1995-01-01
A neuro-fuzzy control model has been devised for the next generation of so-called “intelligent sewing machines”. The model incorporates discrimination of material characteristics to be stitched by automatic determination of their properties. The fabric\\/machine interactions at different speeds have been computed in the form of linguistic rules of a fuzzy model and implemented in a neural network to allow for
Identification of IP packet transmission time series using neuro-fuzzy techniques
Samira Chabaa; Jilali Antari; Abdelouhab Zeroual
2011-01-01
In this paper we are interested to applied the adaptive neuro-fuzzy inference system (ANFIS) technique, which is realized by an appropriate combination of fuzzy systems and neural networks, for identifying and forecasting a set of input and output data of packet transmission over internet protocol (IP) networks. The obtained results demonstrate that the developed model presents the same statistical characteristics
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
NASA Astrophysics Data System (ADS)
Hurtado, N.; López, D.; Costanzo-Alvarez, V.; Aldana, M.
2009-04-01
We have measured NRM, room temperature magnetic susceptibility, S ratios and Königsberger ratios in 134 samples that encompass aproximately 670 meters of depth in an oil well drilled in eastern Colombia. These samples are sandstones and siltstones from the Guayabo, León and Carbonera Formations (Oligocene/Miocene/Pliocene). Our main goal is to asses the potential of the Neuro Fuzzy Logic analysis to infer magnetic parameters such as S ratios and Königsberger ratios from magnetic susceptibility experimental data. This method has been previously used with some success to obtain other petrophysical properties such as permeability out of porosity experimental data, however this is the first time it is applied to bulk magnetic properties. The results obtained here are then compared and integrated with their experimental counterparts. They are also used to study the variability of the paleoenvironmental conditions during the formation of the Barinas Apure sedimentary basin in eastern Colombia and western Venezuela.
Adaptive neuro-fuzzy and fuzzy decision tree classifiers as applied to seafloor characterization
NASA Astrophysics Data System (ADS)
Stepnowski, A.; Moszy?ski, M.; van Dung, Tran
2003-03-01
This paper investigates the influence of various backscattered echo parameters collected at an echosounder frequency of 200 kHz on the performance of the neuro-fuzzy and fuzzy decision tree classifiers of a seabed. In particular, the wavelet coefficients of the bottom echo were investigated along with other echo parameters like energy, amplitude, slope of the falling part of the echo, etc. The data were processed in an Adaptive Neuro Fuzzy Inference System (ANFIS), which was implemented in two multistage structures, viz.; Incremental Fuzzy Neural Network (IFNN) and Aggregated Fuzzy Neural Network (AFNN). The number of input parameters for the networks was reduced by using the Principal Component Analysis (PCA). A fuzzy decision tree algorithm was developed and used directly (without PCA data reduction) in the classification procedure utilizing the same data. The performances of both approaches were analyzed and compared.
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.
Liu, Cheng-Li
2009-05-01
Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE. PMID:19144322
A hybrid neuro-fuzzy power system stabilizer for multimachine power systems
M. A. Abido; Y. L. Abdel-Magid
1998-01-01
A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters
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
Neuro-fuzzy technology as a predictor of parathyroid hormone level in hemodialysis patients.
Chen, Chiou-An; Li, Yu-Chuan; Lin, Yuh-Feng; Yu, Fu-Chiu; Huang, Wei-Hsin; Chiu, Jainn-Shiun
2007-01-01
Measuring the plasma parathyroid hormone (PTH) concentration is crucial to evaluate renal bone disease in patients with renal failure. Although frequent measurement is needed to avoid inadequate prescription of phosphate binders and vitamin D preparations, artificial intelligence can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests. Neuro-fuzzy technology represents a promising forecasting application in clinical medicine. We therefore constructed a coactive neuro-fuzzy inference system (CANFIS) to predict plasma PTH concentrations in hemodialysis patients. The CANFIS was constructed with clinical parameters (patient age, plasma albumin, calcium, phosphorus, alkaline phosphatase, and calcium-phosphorus product) from a cohort of hemodialysis patients, and plasma PTH concentration measured by radioimmunoassay (RIA) was the supervised outcome. The accuracy of the CANFIS was prospectively compared with RIA in another hospital. Plasma PTH concentrations measured by RIA and predicted by CANFIS were 179.04 +/- 38.18 ng/l and 179.34 +/- 37.76 ng/l, respectively (p = 0.15). The CANFIS was able to precisely estimate plasma PTH concentrations in hemodialysis patients. These results suggest that the neuro-fuzzy technology, based on limited clinical parameters, is an excellent alternative to RIA for accurately predicting plasma PTH concentration in hemodialysis patients. PMID:17202775
A neuro-fuzzy system for tool condition monitoring in metal cutting
Mesina, Omez Samoon
1993-01-01
and Linguisnc Labels. . . . II. 1. 3 Fuzzy Inference and Defuzzification II. 1. 4 LR Parainetrization II. 2 Arlificial Neural Networks. II. 2. 1 Basic Aspects of Artificial Neural Networks . . . . . 11. 2. 2 Back Propagation Algorithm. III NEURAL NETWORK.... Fuzzy sets and linguistic labels. . Fig. 3. Defuzzification. Fig. 4. LR parametxization. Fig. 5. (i) An artificial neural network (ii) Basic aspects of an ANN . . . . . Fig. 6. A 4-5-1 neural network to predict tool condition...
Chaochao Chen; George Vachtsevanos; Marcos E. Orchard
Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component\\/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS)
A. Halvaei Niasar; H. Moghbelli; A. Vahedi
2006-01-01
Principle of a new adaptive neuro-fuzzy inference system (ANFIS) with supervisory learning algorithm is introduced and is used to regulate the speed of a four-switch, three-phase inverter (FSTPI) brushless DC (BLDC) drive. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of drive's performance, instead of well-known back propagation learning method, a fuzzy based supervisory learning algorithm
Neuro-fuzzy Methodology for Selecting Genes Mediating Lung Cancer
Rajat K. De; Anupam Ghosh
\\u000a In this article, we describe neuro-fuzzy models under supervised and unsupervised learning for selecting a few possible genes\\u000a mediating a disease. The methodology involves grouping of genes based on correlation coefficient using microarray gene expression\\u000a patterns. The most important group is selected using existing neuro-fuzzy systems [1,2,3,4,5]. Finally, a few possible genes\\u000a are selected from the most important group using
NASA Astrophysics Data System (ADS)
Prakash, S.; Sinha, S. K.
2014-08-01
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.
Development of a neuro-fuzzy expert system for predictive maintenance
NASA Astrophysics Data System (ADS)
Yen, Gary G.; Meesad, Phayung
2001-07-01
In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly mapped into if-then rule bases. A knowledge base for fuzzy expert systems can then be extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only important features of input patterns needed to provide to a fuzzy rule-based system. Three computer simulations using the Wisconsin breast cancer data set were performed. Using 400 patterns for training and 299 patterns for testing, the derived fuzzy expert system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.
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.
Ovidiu Grigore; Adriana Florescu; Alexandru Vasile; Dan Alexandru Stoichescu
The paper compares fuzzy and neuro-fuzzy designs of a duty-cycle compensation controller used to linearize the nonlinear external characteristics family of a step-down (Buck) or forward DC-DC converter that supplies DC motors. This controller is additionally introduced in high precision speed control systems. Comparison reveals the advantages of neuro-fuzzy controllers upon fuzzy controllers. A discussion on real-time implementation is also
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
Fuzzy and neuro-fuzzy designs of boost converter supplying DC motors
DRAGOS VASILE; A. Florescu; D. A. Stoichescu
2000-01-01
The paper compares fuzzy and neuro-fuzzy designs of a duty-cycle compensation controller used to linearize the nonlinear external characteristics family of a boost converter that supplies DC motors. This controller is additionally introduced in high precision speed control systems. Comparison reveals the advantages of neuro-fuzzy controllers over fuzzy controllers. A discussion on real-time implementation is also taken under consideration
NASA Astrophysics Data System (ADS)
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Bhatt, Rajen B; Gopal, M
2006-02-01
Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets. PMID:16496439
Landslide susceptibility mapping using a neuro-fuzzy
NASA Astrophysics Data System (ADS)
Lee, S.; Choi, J.; Oh, H.
2009-12-01
This paper develops and applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. A neuro-fuzzy system is based on a fuzzy system that is trained by a learning algorithm derived from the neural network theory. The learning procedure operates on local information, and causes only local modifications in the underlying fuzzy system. The study area, Boun, suffered much damage following heavy rain in 1998 and was selected as a suitable site for the evaluation of the frequency and distribution of landslides. Boun is located in the central part of Korea. Landslide-related factors such as slope, soil texture, wood type, lithology, and density of lineament were extracted from topographic, soil, forest, and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs) and analysis results were verified using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping. Various MFs were used in this study, and after verification, the difference in accuracy according to the MFs was small, between 84.81% and 84.96%. The difference was just 0.15% and therefore the choice of MFs was not important in the study. Also, compared with the likelihood ratio model, which showed 84.94%, the accuracy was similar. Thus, the ANFIS could be applied to other study areas with different data and other study methods such as cross-validation. The developed ANFIS learns the if-then rules between landslide-related factors and landslide location for generalization and prediction. It is easy to understand and interpret, therefore it is a good choice for modeling landslide susceptibility mapping, which are also of great help for planners and engineers in selecting highly susceptible areas for further detail surveys and suitable locations to implement development. Although they may be less useful at the site-specific scale, where local geological and geographic heterogeneities may prevail, the results herein may be used as basic data to assist slope management and land use planning. For the method to be more generally applied, more landslide data are needed and more case studies should be conducted.
Reyneri, Leonardo
-Fuzzy Processor Selector Finite State Automata Plant discrete states Actuators Sensors Parameters of specialized with Finite State Automata FSA see gure 1. The FSA tracks the state of the plant and selects the parameter e intelligent control al- gorithms mixing neuro-fuzzy algorithms with nite state automata and is used to control
Daily soil temperature modeling using neuro-fuzzy approach
NASA Astrophysics Data System (ADS)
Hosseinzadeh Talaee, P.
2014-11-01
Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models' performances at arid and semiarid locations showed that the CANFIS models' performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.
Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T; Kiaei, Ali A
2010-01-01
In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation of the facial expressions, and 3) by continuous values of intensity and employing top-down hierarchical rule-based classifiers, we can develop accurate human-interpretable AU-to-expression converters. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with support vect...
Recognition of Handwritten Arabic words using a neuro-fuzzy network
Boukharouba, Abdelhak [Departement de Genie electrique, Universite 08 Mai 45 de Guelma (Algeria); Bennia, Abdelhak [Departement d'Electronique, Universite Mentouri de Constantine (Algeria)
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 mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.
Neuro-Fuzzy Control of a Robotic Manipulator
NASA Astrophysics Data System (ADS)
Gierlak, P.; Muszy?ska, M.; ?ylski, 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
M. Samhouri; A. Al-Ghandoor; S. A. Ali; I. Hinti; W. Massad
Monitoring and predicting machine components' faults play an important role in maintenance actions. Developing an intelligent system is a good way to overcome the problems of maintenance management. In fact, several methods of fault diagnostics have been developed and applied effectively to identify the machine faults at an early stage using different quantities (Measures or Readings) such as current, voltage,
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
Suction Control of a Robotic Gripper: A Neuro-Fuzzy Approach
Nikos C. Tsourveloudis; Ramesh Kolluru; Kimon P. Valavanis; Denis Gracanin
2000-01-01
This paper discusses a fuzzy logic control system designed to determine, regulate and maintain the amount of suction needed by a robotic gripper system to perform reliable limp material manipulation. A neuro-fuzzy approach is followed to determine the amount of desired suction (depending on experimentally derived data and plant characteristics). A knowledge-based valve controller is then designed to generate, regulate
Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines
are working to replace these highly inefficient drives with electronic, variable-speed control systems capableNeuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines Kasim M. Al and performance of a sensorless PM motor drive for washing machines. An implicit rotor position detection unit
NEURO-FUZZY BASED FAULT DIAGNOSIS APPLIED TO AN ELECTRO-PNEUMATIC VALVE
Faisel J Uppal; Ron J Patton; Vasile Palade
The early detection of faults (just beginning and still developing) can help avoid system shutdown, breakdown and even catastrophes involving hu man fatalities and material damage. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the properties of the TSK\\/Mamdani approaches and neuro-fuzzy (NF) fault diagnosis within an application study of an
Fábio Martins; K. Figueiredo; M. Vellasco
2010-01-01
This paper presents two methods for accelerating the learning process of Reinforcement Learning - Neuro-Fuzzy Hierarchical Politree model (RL-NFHP): policy Q-DC-Roulette and early stopping. This model is used to provide an agent with intelligence, making it autonomous, due to the capacity of ratiocinate (infer actions) and learning, acquired knowledge through interaction with the environment. The characteristics of the RL-NFHP allow
Ekrem Kalkan; Suat Akbulut; Ahmet Tortum; Samet Celik
2009-01-01
Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have been extensively used to predict\\u000a different soil properties in geotechnical applications. In this study, it was aimed to develop ANFIS and ANN models to predict\\u000a the unconfined compressive strength (UCS) of compacted soils. For this purpose, 84 soil samples with different grain-size\\u000a distribution compacted at optimum water content
Yuan-ruey Huang; Yuan Kang; Ming-hui Chu; Shu-yen Chien; Yeon-pun Chang
2009-01-01
This paper proposes a Chebyshev functional recurrent neuro-fuzzy (CFRNF) network to identify a nonlinear system, which is composed of nine layers network and a six-layer Chebyshev recurrent neural network (CRNN) used to emulate nonlinear system is one of nine layers. Based on Takagi–Sugeno–Kang (TSK) fuzzy model, the nonlinear dynamics of this system can be addressed by enhancing the input dimensions
NASA Astrophysics Data System (ADS)
Dixon, B.
2005-07-01
Modeling groundwater vulnerability reliably and cost effectively for non-point source (NPS) pollution at a regional scale remains a major challenge. In recent years, Geographic Information Systems (GIS), neural networks and fuzzy logic techniques have been used in several hydrological studies. However, few of these research studies have undertaken an extensive sensitivity analysis. The overall objective of this research is to examine the sensitivity of neuro-fuzzy models used to predict groundwater vulnerability in a spatial context by integrating GIS and neuro-fuzzy techniques. The specific objectives are to assess the sensitivity of neuro-fuzzy models in a spatial domain using GIS by varying (i) shape of the fuzzy sets, (ii) number of fuzzy sets, and (iii) learning and validation parameters (including rule weights). The neuro-fuzzy models were developed using NEFCLASS-J software on a JAVA platform and were loosely integrated with a GIS. Four plausible parameters which are critical in transporting contaminants through the soil profile to the groundwater, included soil hydrologic group, depth of the soil profile, soil structure (pedality points) of the A horizon, and landuse. In order to validate the model predictions, coincidence reports were generated among model inputs, model predictions, and well/spring contamination data for NO 3-N. A total of 16 neuro-fuzzy models were developed for selected sub-basins of Illinois River Watershed, AR. The sensitivity analysis showed that neuro-fuzzy models were sensitive to the shape of the fuzzy sets, number of fuzzy sets, nature of the rule weights, and validation techniques used during the learning processes. Compared to bell-shaped and triangular-shaped membership functions, the neuro-fuzzy models with a trapezoidal membership function were the least sensitive to the various permutations and combinations of the learning and validation parameters. Over all, Models 11 and 8 showed relatively higher coincidence with well contamination data than other models. The strength of this method is that it offers a means of dealing with imprecise data, therefore, is a viable option for regional and continental scale environmental modeling where imprecise data prevail. The neuro-fuzzy models, however, should only be used as a tool within a broader framework of GIS, remote sensing and solute transport modeling to assess groundwater vulnerability along with functional, mechanistic and stochastic models.
A neuro-fuzzy controller for axial power distribution an nuclear reactors
Man Gyun Na; B. R. Upadhyaya
1998-01-01
A neuro-fuzzy control algorithm is applied for the core power distribution in a pressurized water reactor. The inputs of the neural fuzzy system are composed of data from each region of the reactor core. Rule outputs consist of linear combinations of their inputs (first-order Sugeno-Takagi type). The consequent and antecedent parameters of the fuzzy rules are updated by the backpropagation
Masoud Sadeghian; Alireza Fatehi
2009-01-01
In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
Shamshirband, Shahaboddin; Petkovi?, 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
Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine
Shamshirband, Shahaboddin; Petkovi?, 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
Controlling Interstate Conflict using Neuro-fuzzy Modeling and Genetic Algorithms
T. Tettey; T. Marwala
2006-01-01
The paper introduces neuro-fuzzy modeling to the problem of controlling interstate conflict. It is shown that a neuro-fuzzy model achieves a prediction accuracy similar to Bayesian trained neural networks. It is further illustrated that a neuro-fuzzy model can be used in a genetic algorithm (GA) based control scheme to avoid 100% of the detected conflict cases. The neuro-fuzzy model is
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.
Neuro-fuzzy logic based fusion algorithm of medical images
Jionghua Teng; Suhuan Wang; Jingzhou Zhang; Xue Wang
2010-01-01
CT, single photon emission computed tomography (SPECT) and nuclear magnetic resonance imaging (MRI) are complementary on reflecting human information. In order to provide more useful information for clinical diagnosis, we have a need to fuse the effective information. In the pixel-level fusion between the medical images, we presented a fusion algorithm based on neuro-fuzzy logic in this paper, and utilized
Neuro-Fuzzy Control for MPEG Video Transmission Over Bluetooth
Hassan B. Kazemian; Li Meng
2006-01-01
The application of a neuro-fuzzy (NF) controller to moving picture expert group (MPEG-2) video transmission over a Bluetooth asynchronous connectionless (ACL) is presented in this paper. MPEG variable bit rate (VBR) data sources experience unpredictability, long delay, and excessive loss, due to sudden variations in bit rate. Therefore, it is practically impossible to transmit MPEG-2 VBR video sources over a
NASA Astrophysics Data System (ADS)
Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina
2013-04-01
Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.
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.
A. L. Tamiru; C. Rangkuti; F. M. Hashim
2009-01-01
Developing a first principle nonlinear model for a thermal system that is already in operation is a very difficult task attributed to missing design parameters. This paper considers nonlinear modeling of subunits of a Cogeneration and Cooling Plant (CCP)-Heat Recovery Steam Generator (HRSG), Steam Header (SH) and Steam Absorption Chiller (SAC). Neuro-fuzzy approach trained by a sequence of optimization algorithms-Particle
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.
Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter
2012-07-01
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047
Paris-Sud XI, Université de
and environmental ones for the optimization of processes. This major provocation of triple performance outlined a prognostics system with no human intervention, neither a priori knowledge? The proposition is based on the use of reliability, availability or safety of a system is a determining factor in regard with the effectiveness
NASA Astrophysics Data System (ADS)
Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.
2012-04-01
Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.
NASA Astrophysics Data System (ADS)
Oh, Hyun-Joo; Pradhan, Biswajeet
2011-09-01
This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.
Neuro-fuzzy CBR hybridization: Healthcare application
Joseph M. Woodside
2008-01-01
As the total cost of healthcare continues to rise, computerized methods are sought to improve the overall efficiency and effectiveness of healthcare systems. In this application, the focus is on healthcare claim payment processing, which is a major component of administrative healthcare costs. Due to the complexity of healthcare data, current methods require a large amount of healthcare claim payment
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 determination of the landslide susceptibility map.
A neuro-fuzzy approach in the classification of students' academic performance.
Do, Quang Hung; Chen, Jeng-Fung
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
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
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petkovi?, 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
A reinforcement neuro-fuzzy combiner for multiobjective control.
Lin, C T; Chung, I F
1999-01-01
This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme. PMID:18252353
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.
Lee, Wookgee; Karwowski, Waldemar; Marras, William S; Rodrick, David
2003-01-15
The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics. PMID:12554412
Barenboim, Maxim; Masso, Majid; Vaisman, Iosif I; Jamison, D Curtis
2008-06-01
There is substantial interest in methods designed to predict the effect of nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein function, given their potential relationship to heritable diseases. Current state-of-the-art supervised machine learning algorithms, such as random forest (RF), train models that classify single amino acid mutations in proteins as either neutral or deleterious to function. However, it is frequently the case that the functional effect of a polymorphism on a protein resides between these two extremes. The utilization of classifiers that incorporate fuzzy logic provides a natural extension in order to account for the spectrum of possible functional consequences. We generated a dataset of single amino acid substitutions in human proteins having known three-dimensional structures. Each variant was uniquely represented as a feature vector that included computational geometry and knowledge-based statistical potential predictors obtained though application of Delaunay tessellation of protein structures. Additional attributes consisted of physicochemical properties of the native and replacement amino acids as well as topological location of the mutated residue position in the solved structure. Classification performance of the RF algorithm was evaluated on a training set consisting of the disease-associated and neutral nsSNPs taken from our dataset, and attributes were ranked according to their relative importance. Similarly, we evaluated the performance of adaptive neuro-fuzzy inference system (ANFIS). The utility of statistical geometry predictors was compared with that of traditional structural and evolutionary attributes employed by other researchers, revealing an equally effective yet complementary methodology. Among all attributes in our feature set, the statistical geometry predictors were found to be the most highly ranked. On the basis of the AUC (area under the ROC curve) measure of performance, the ANFIS and RF models were equally effective when only statistical geometry features were utilized. Tenfold cross-validation studies evaluating AUC, balanced error rate (BER), and Matthew's correlation coefficient (MCC) showed that our RF model was at least comparable with the well-established methods of SIFT and PolyPhen. The trained RF and ANFIS models were each subsequently used to predict the disease potential of human nsSNPs in our dataset that are currently unclassified (http://rna.gmu.edu/FuzzySnps/). PMID:18186470
NASA Astrophysics Data System (ADS)
Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.
2012-04-01
The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Granada, Universidad de
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models Jose´ Luis of Botany, University of Granada, Spain Abstract Forecasting airborne pollen concentrations is one-fuzzy models. In this work, we applied some of these models to forecast olive pollen concentrations
A method of neuro-fuzzy computing for effective fault diagnosis
Tianqi Yang
2005-01-01
The purpose of this paper is to present results that were obtained in fault diagnosis of an industrial process. The diagnosis algorithm is based on a three-layer neuro-fuzzy network theory. We present a new technique for the treatment of overlaps among adjoining fuzzy sets. Inputs of the network are the process I\\/O data, such as pressure and temperature, parameters estimated
An Integrated Neuro-Fuzzy Approach to MPEG Video Transmission in Bluetooth
H. B. Kazemian; S. Chantaraskul
2007-01-01
This paper presents an integrated neuro-fuzzy (NF) scheme and a rule-based-fuzzy (RBF) scheme applications to Moving Picture Expert Group (MPEG) video transmission in Bluetooth. In a Bluetooth network, transmission rate is unpredictable due to interferences by other wireless devices or general Bluetooth channel noises. MPEG variable bit rate (VBR) video transmission is also unreliable and presents long delay and excessive
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet
2013-02-01
The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.
Streamflow Forecasting Using Nuero-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Nanduri, U. V.; Swain, P. C.
2005-12-01
The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.
Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed
NASA Astrophysics Data System (ADS)
Olivier, Philip D.
2002-07-01
NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.
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
M. Samhouri; A. Al-Ghandoor; R. H. Fouad
2009-01-01
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
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.
Learning in a neuro-fuzzy navigator for robotic manipulators
K. Althoefer; L. Seneviratne; B. Krekelberg
1999-01-01
Presents a fuzzy-based navigation system for robotic manipulators. The fuzzy rules combine a repelling influence, related to the distance between the manipulator and nearby obstacles, with an attracting influence produced by the angular difference between the actual and final manipulator configurations, in order to generate the actuating motor commands. The use of fuzzy logic leads to a transparent system that
Neuro-fuzzy TSK network for approximation Stanislaw Osowski
Osowski, Stanislaw
the neurofuzzy network in application to the approximation of the static and dy- namic functions. The network of static nonlinear relations and on-line simulation of the dynamic systems. 1 Introduction In last years, 7, 11]. Two groups of approaches can be recognized. One group consists of neural networks with fuzzy
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...
Adaptive neuro-fuzzy fusion of sensor data
NASA Astrophysics Data System (ADS)
Petkovi?, 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.
A neuro-fuzzy approach for segmentation of human objects in image sequences.
Lee, Shie-Jue; Ouyang, Chen-Sen; Du, Shih-Huai
2003-01-01
We propose a novel approach for segmentation of human objects, including face and body, in image sequences. Object segmentation is important for achieving a high compression ratio in modern video coding techniques, e.g., MPEG-4 and MPEG-7, and human objects are usually the main parts in the video streams of multimedia applications. Existing segmentation methods apply simple criteria to detect human objects, leading to the restriction of the usage or a high segmentation error. We combine temporal and spatial information and employ a neuro-fuzzy mechanism to overcome these difficulties. A fuzzy self-clustering technique is used to divide the base frame of a video stream into a set of segments which are then categorized as foreground or background based on a combination of multiple criteria. Then, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network constructed with the fuzzy rules previously obtained and is trained by a singular value decomposition (SVD)-based hybrid learning algorithm. The proposed approach has been tested on several different video streams, and the results have shown that the approach can produce a much better segmentation than other methods. PMID:18238189
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
Neurofuzzy network modelling and control steam pressure in 300 MW steam-boiler system
Xiang-Jie Liu; Felipe Lara-Rosano
2003-01-01
Modelling and control of 300 MW steam-boiler combustion system using neuro-fuzzy methodology is discussed in this paper. An associate memory network (AMN) is chosen to represent the nonlinear model of the steam pressure system based on local mechanism model and dynamic experiments. With the established neuro-fuzzy model, a relative fuzzy PI controller is constituted. The performance of the control system
Lan, T H; Loh, E W; Wu, M S; Hu, T M; Chou, P; Lan, T Y; Chiu, H-J
2008-12-01
Artificial intelligence has become a possible solution to resolve the problem of loss of information when complexity of a disease increases. Obesity phenotypes are observable clinical features of drug-naive schizophrenic patients. In addition, atypical antipsychotic medications may cause these unwanted effects. Here we examined the performance of neuro-fuzzy modeling (NFM) in predicting weight changes in chronic schizophrenic patients exposed to antipsychotics. Two hundred and twenty inpatients meeting DSMIV diagnosis of schizophrenia, treated with antipsychotics, either typical or atypical, for more than 2 years, were recruited. All subjects were assessed in the same study period between mid-November 2003 and mid-April 2004. The baseline and first visit's physical data including weight, height and circumference were used in this study. Clinical information (Clinical Global Impression and Life Style Survey) and genotype data of five single nucleotide polymorphisms were also included as predictors. The subjects were randomly assigned into the first group (105 subjects) and second group (115 subjects), and NFM was performed by using the FuzzyTECH 5.54 software package, with a network-type structure constructed in the rule block. A complete learned model trained from merged data of the first and second groups demonstrates that, at a prediction error of 5, 93% subjects with weight gain were identified. Our study suggests that NFM is a feasible prediction tool for obesity in schizophrenic patients exposed to antipsychotics, with further improvements required. PMID:18180752
Biogeography-Based Optimization of Neuro-Fuzzy System Parameters for Diagnosis of Cardiac Disease
Simon, Dan
, the heart chambers may stretch or dilate. Valvular regurgitation and congestive heart failure are two, which in turn predisposes the heart to failure or arrhythmias. Cardiomyopathy in its two common forms Cleveland, Ohio d.j.simon@csuohio.edu ABSTRACT Cardiomyopathy refers to diseases of the heart muscle
Recognition of gestures in Arabic sign language using neuro-fuzzy systems
Omar M. Al-jarrah; Alaa Halawani
2001-01-01
Hand gestures play an important role in communication between people during their daily lives. But the extensive use of hand gestures as a mean of communication can be found in sign languages. Sign language is the basic communication method between deaf people. A translator is usually needed when an ordinary person wants to communicate with a deaf one. The work
Flood Forecasting in River System Using ANFIS
Ullah, Nazrin; Choudhury, P. [Dept. of Civil Eng., NIT, Silchar (India)
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.
Using Neuro-Fuzzy Techniques to reduce false alerts in IDS
Pravesh Gaonjur; N. Z. Tarapore; S. G. Pukale; M. L. Dhore
2008-01-01
The problems related to security for network systems are relative to the design of network architectures, which is typically based on open standards. Monitoring tools based on pattern recognition or behavioral analysis is typically used to ensure network security. SNORT is one such tool which is based on pattern recognition. SNORT alerts system administrators whenever it receives packets of information
Application of Neuro-fuzzy Network for Fault Diagnosis in an Industrial Process
Tianqi Yang
2007-01-01
The purpose of this paper is to present results that were obtained in fault diagnosis of an industrial process. The diagnosis algorithm combines an artificial neural network (ANN) based supplement of a fuzzy system in a block-oriented configuration. A methodology for designing the system is described. As a motivating example, a chemical plant with a recycle stream is considered. Faults
A new control architecture in mobile robot navigation based on IT2neuro-fuzzy controller
Siti Nurmaini; Bambang Tutuko
2011-01-01
Autonomous mobile robots navigating in changing and unknown environments need to cope with large amounts of uncertainties that are inherent in natural environments. Most of the controllers have some common drawbacks such as, large computation, expensive equipment, hard implementation, and the complexity of the system. In this paper, novel controller architecture is introduced based on interval type-2 fuzzy logic controller
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
NASA Astrophysics Data System (ADS)
Saadeddin, Kamal; Abdel-Hafez, Mamoun F.; Jaradat, Mohammad A.; Jarrah, Mohammad Amin
2013-12-01
In this paper, a low-cost navigation system that fuses the measurements of the inertial navigation system (INS) and the global positioning system (GPS) receiver is developed. First, the system's dynamics are obtained based on a vehicle's kinematic model. Second, the INS and GPS measurements are fused using an extended Kalman filter (EKF) approach. Subsequently, an artificial intelligence based approach for the fusion of INS/GPS measurements is developed based on an Input-Delayed Adaptive Neuro-Fuzzy Inference System (IDANFIS). Experimental tests are conducted to demonstrate the performance of the two sensor fusion approaches. It is found that the use of the proposed IDANFIS approach achieves a reduction in the integration development time and an improvement in the estimation accuracy of the vehicle's position and velocity compared to the EKF based approach.
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
NASA Astrophysics Data System (ADS)
Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.
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.
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.
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.
Jose Antonio Piedra; Manuel Canton; Francisco Guindos
Our goal in this work has been to study methods for the automatic interpretation of ocean satellite images by means of the recognition of meso and macroscalar ocean structures. The difficulty of the image analysis and understanding problem for ocean satellite data is due, in large part, to the lack of a precise mathematical description of the observed structures and
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.
Chakraborty, Debrup
manner. It is a five-layered feed-forward network for realizing a fuzzy rule-based system. The second 391 Integrated Feature Analysis and Fuzzy Rule-Based System Identification in a Neuro-Fuzzy Paradigm Debrup Chakraborty and Nikhil R. Pal, Senior Member, IEEE Abstract--Most methods of fuzzy rule
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.
Rafael Toledo-Moreo; Miguel Pinzolas-Prado; Jose Manuel Cano-Izquierdo
2010-01-01
Collision avoidance is currently one of the main research areas in road intelligent transportation systems. Among the different possibilities available in the literature, the prediction of abrupt maneuvers has been shown to be useful in reducing the possibility of collisions. A supervised version of dynamic Fuzzy Adaptive System ART-based (dFasArt), which is a neuronal-architecture-based method that employs dynamic activation functions
Masoud Sadeghian; Alireza Fatehi
2009-01-01
One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not
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.
Challenges for context management systems imposed by context inference
Korbinian Frank; Nikos Kalatzis; Ioanna Roussaki; Nicolas Liampotis
2009-01-01
This work gives an overview over the challenges for context management systems in Ubiquitous Computing frameworks or Personal Smart Spaces. Focused on the integration of context inference in today's context management systems (CMSs) we address important design decisions for future frameworks. The inference system we have in mind is probabilistic and relies on the concept of Bayeslets, special inference rules
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.
Grey box modelling and advanced control scheme for building heating systems
NASA Astrophysics Data System (ADS)
Jassar, Surinder
This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modeling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design an inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on the measurement of air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.
FPGA Implementation of Fuzzy Inference System for Embedded Applications
FPGA Implementation of Fuzzy Inference System for Embedded Applications Dr. Kasim M. Al the software program stack to embedded processors on the FPGA to improve performance and reduce the cost of the whole system. A fuzzy inference system has been implemented on an FPGA, and used to control a PM motor
Inference aggregation detection in database management systems
T. H. Hinke
1988-01-01
The author identifies inference aggregation and cardinality aggregation as two distinct aspects of the aggregation problem. He develops the concept of a semantic relationship graph to describe the relationships between data and then presents inference aggregation as the problem of finding alternative paths between vertices on the graph. He presents an algorithm for processing the semantic relationship graph to discover
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.
Inference by replication in densely connected systems
Neirotti, Juan P.; Saad, David [The Neural Computing Research Group, Aston University, Birmingham B4 7ET (United Kingdom)
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.
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…
Malik, Owais A; Arosha Senanayake, S M N; 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
ANFIS: adaptive-network-based fuzzy inference system
Jyh-Shing Roger Jang
1993-01-01
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation,
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.
An alternative respiratory sounds classification system utilizing artificial neural networks.
Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen
2014-09-01
Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques. PMID:25179722
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
Rafael Toledo; Miguel Pinzolas; J. Manuel Cano Izquierdo
2007-01-01
A supervised version of dFasArt, a neuronal architecture based method that employs dynamic activation functions determined\\u000a by fuzzy sets is used for solving support of the problem of inter-vehicles collisions in roads. The dynamic character of dFasArt\\u000a minimizes problems caused by noise in the sensors and provides stability on the predicted maneuvers. To test the proposed\\u000a algorithm, several experiments with
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 ?g/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
S. Barghinia; S. Kamankesh; N. Mahdavi; A. H. Vahabie; A. A. Gorji
2008-01-01
Short term load forecasting (STLF) plays an important role for the power system operational planners and also most of the participants in the nowadays electricity markets. With the importance of the STLF in power system operation and electricity markets, many methods for arriving careful results, are represented. In this paper, a combination approach for STLF is proposed. The proposed approach
Efficient Concurrent Simulation of DEVS Systems Based on Concurrent Inference
Manuel Cabarcos; Ramón P. Otero; Silvia Gómez Pose
1999-01-01
\\u000a This paper studies the concurrent simulation of DEVS systems by using their encoding into a formalism for dynamic systems\\u000a called Generalized Magnitudes (GM). When represented with GMs, the internal parallelism of a DEVS model is foregrounded and\\u000a the concurrent inference techniques developed for the GMs formalism can be applied to speed up simulation. This approach is\\u000a used for both atomic
Inference and learning in sparse systems with multiple states
Braunstein, A. [Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy); Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Ramezanpour, A.; Zhang, P. [Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Zecchina, R. [Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy); Collegio Carlo Alberto, Via Real Collegio 30, I-10024 Moncalieri (Italy)
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.
Inference and learning in sparse systems with multiple states.
Braunstein, A; Ramezanpour, A; Zecchina, R; Zhang, P
2011-05-01
We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost. PMID:21728612
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.
Adaptation of CDMA soft handoff thresholds using fuzzy inference system
Bongkarn Homnan; V. Kunsriruksakul; W. Benjapolakul
2000-01-01
This paper proposes a new procedure to adjust soft handoff thresholds (T-DROP, T-ADD) by using a fuzzy inference system (FIS). The aims are to increase the value of T-DROP in order to release the traffic channel (TCH) at high traffic loads for increasing the carried traffic, and to decrease the value of T-DROP in order to add the TCH in
Topological augmentation to infer hidden processes in biological systems
Sunnåker, Mikael; Zamora-Sillero, Elias; López García de Lomana, Adrián; Rudroff, Florian; Sauer, Uwe; Stelling, Joerg; Wagner, Andreas
2014-01-01
Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24297519
Platform-Based Inference System Design Using FML and Fuzzy Technology for Healthcare
Chen-Yu Chen; Mingzoo Wu; Chi-Lu Yang; Shing-Hua Ho; Chin-Yuan Hsu
2009-01-01
This study presents a framework of platform-based inference system design to provide specific field experts\\/users developing customized fuzzy inference system efficiently and effectively. The framework is composed of three major components: FML editor, FML parser and FML inference engine. First, the FML editor can let field experts compile knowledge base and rule base with friendly user interface. The composed FML
Improving real time flood forecasting using fuzzy inference system
NASA Astrophysics Data System (ADS)
Lohani, Anil Kumar; Goel, N. K.; Bhatia, K. K. S.
2014-02-01
In order to improve the real time forecasting of foods, this paper proposes a modified Takagi Sugeno (T-S) fuzzy inference system termed as threshold subtractive clustering based Takagi Sugeno (TSC-T-S) fuzzy inference system by introducing the concept of rare and frequent hydrological situations in fuzzy modeling system. The proposed modified fuzzy inference systems provide an option of analyzing and computing cluster centers and membership functions for two different hydrological situations, i.e. low to medium flows (frequent events) as well as high to very high flows (rare events) generally encountered in real time flood forecasting. The methodology has been applied for flood forecasting using the hourly rainfall and river flow data of upper Narmada basin, Central India. The available rainfall-runoff data has been classified in frequent and rare events and suitable TSC-T-S fuzzy model structures have been suggested for better forecasting of river flows. The performance of the model during calibration and validation is evaluated by performance indices such as root mean square error (RMSE), model efficiency and coefficient of correlation (R). In flood forecasting, it is very important to know the performance of flow forecasting model in predicting higher magnitude flows. The above described performance criteria do not express the prediction ability of the model precisely from higher to low flow region. Therefore, a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model. The developed model has been tested for different lead periods using hourly rainfall and discharge data. Further, the proposed fuzzy model results have been compared with artificial neural networks (ANN), ANN models for different classes identified by Self Organizing Map (SOM) and subtractive clustering based Takagi Sugeno fuzzy model (SC-T-S fuzzy model). It has been concluded from the study that the TSC-T-S fuzzy model provide reasonably accurate forecast with sufficient lead-time.
An expert system shell for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1993-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this contract, an interface to a file of unknown cover type data was constructed. An interface that allowed the results of VEG to be written to a file was also implemented. A learning system that learned class descriptions from a data base of historical cover type data and then used the learned class descriptions to classify an unknown sample was built. This system had an interface that integrated it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER was completed and a number of additional techniques that inferred the proportion ground cover of a sample were implemented. This work was previously described. The work carried out in the second year of the contract is described. The historical cover type database was removed from VEG and stored as a series of flat files that are external to VEG. An interface to the files was provided. The framework and interface for two new VEG subgoals that estimate the atmospheric effect on reflectance data were built. A new interface that allows the scientist to add techniques to VEG without assistance from the developer was designed and implemented. A prototype Help System that allows the user to get more information about each screen in the VEG interface was also added to VEG.
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
Inference Attacks by Third-Party Extensions to Social Network Systems
, but they are not the focus of this work. Extensibility gives rise to new security and privacy chal- lenges: Since third by exploring one particular class of attacks enabled by the extension API of a Social Network System (SNS), namely, the class of SNS API inference attacks. We define an SNS API inference attack to be the inference
Computational Intelligence for Medical Knowledge Acquisition with Application to Glaucoma
Ulieru, Mihaela
Computational Intelligence for Medical Knowledge Acquisition with Application to Glaucoma Nicolae of glaucoma. The knowledge acquired is embedded in a fuzzy logic inference system. The resulting Neuro-fuzzy Glaucoma Diagnosis and Prediction System is expected to lower the effort, difficulties and risk cost
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
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
An on-line classification approach of visitors' movements in 3D virtual museums
Thawonmas, Ruck
on an animals' behavior metaphor are ant, fish, grasshopper, and butterfly styles. The ant visitors spend quite deals with the simulation of four visitors' styles, i.e., ant, fish, grasshopper, and butterfly, and the classification of those four styles using an Adaptive Neuro-Fuzzy Infer- ence System (ANFIS). First, we analyze
An on-line classification approach of visitors' movements in 3D virtual museums
Boyer, Edmond
visiting styles based on an animals' behavior metaphor are ant, fish, grasshopper, and butterfly styles deals with the simulation of four visitors' styles, i.e., ant, fish, grasshopper, and butterfly, and the classification of those four styles using an Adaptive Neuro-Fuzzy Infer- ence System (ANFIS). First, we analyze
FPGA-Based Fuzzy Inference System for Real-time Embedded Applications
FPGA-Based Fuzzy Inference System for Real-time Embedded Applications Dr. Kasim M. Al be loaded from the software program stack to embedded processors on the FPGA to improve performance and reduce the cost of the whole system. A fuzzy inference system has been implemented on an FPGA, and used
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
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.
DYNAMICAL INFERENCE FROM A KINEMATIC SNAPSHOT: THE FORCE LAW IN THE SOLAR SYSTEM
Bovy, Jo; Hogg, David W. [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Murray, Iain, E-mail: jo.bovy@nyu.ed [Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4 (Canada)
2010-03-10
If a dynamical system is long-lived and non-resonant (that is, if there is a set of tracers that have evolved independently through many orbital times), and if the system is observed at any non-special time, it is possible to infer the dynamical properties of the system (such as the gravitational force or acceleration law) from a snapshot of the positions and velocities of the tracer population at a single moment in time. In this paper, we describe a general inference technique that solves this problem while allowing (1) the unknown distribution function of the tracer population to be simultaneously inferred and marginalized over, and (2) prior information about the gravitational field and distribution function to be taken into account. As an example, we consider the simplest problem of this kind: we infer the force law in the solar system using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for the eight major planets. We consider purely radial acceleration laws of the form a{sub r} = -A [r/r{sub 0}]{sup -a}lpha, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < alpha < 2.052 (95% interval), largely independent of any assumptions about the distribution of energies and eccentricities in the system beyond the assumption that the system is phase-mixed. Generalizations of the methods used here will permit, among other things, inference of Milky Way dynamics from Gaia-like observations.
Dynamical Inference from a Kinematic Snapshot: The Force Law in the Solar System
NASA Astrophysics Data System (ADS)
Bovy, Jo; Murray, Iain; Hogg, David W.
2010-03-01
If a dynamical system is long-lived and non-resonant (that is, if there is a set of tracers that have evolved independently through many orbital times), and if the system is observed at any non-special time, it is possible to infer the dynamical properties of the system (such as the gravitational force or acceleration law) from a snapshot of the positions and velocities of the tracer population at a single moment in time. In this paper, we describe a general inference technique that solves this problem while allowing (1) the unknown distribution function of the tracer population to be simultaneously inferred and marginalized over, and (2) prior information about the gravitational field and distribution function to be taken into account. As an example, we consider the simplest problem of this kind: we infer the force law in the solar system using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for the eight major planets. We consider purely radial acceleration laws of the form ar = -A [r/r 0]-?, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < ? < 2.052 (95% interval), largely independent of any assumptions about the distribution of energies and eccentricities in the system beyond the assumption that the system is phase-mixed. Generalizations of the methods used here will permit, among other things, inference of Milky Way dynamics from Gaia-like observations.
From free energy measurements to free energy inference in small systems Felix Ritort
Potsdam, UniversitÃ¤t
From free energy measurements to free energy inference in small systems Abstract. Fluctuation theorems (FTs) have provided new methods to extract free the knowledge of the free energy of nucleic acid and protein structures
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.
Cloud-computing-based framework for multi-camera topology inference in smart city sensing system
Ye Wen; Xiaokang Yang; Yi Xu
2010-01-01
This paper proposes a cloud-computing-based algorithmic framework which is scalable and adaptive to online smart city video sensing system. One of the most cost-expensive works in such a system is to infer the topology structure of video camera network, thus spatio-temporal relationship inference for large-scale camera network is simulated on a cloud-computing platform to validate the proposed framework. The simulation
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
The use of tree derivatives and a sample support parameter for inferring tree systems.
Levine, B
1982-01-01
Tree systems have been studied in the theoretical setting as an extension of finite automata. They have been found useful in the practical domain when applied to syntactic pattern recognition. The practical applications of tree systems have motivated the examination of inference techniques for tree grammars and tree automata. In this paper we present a tree automaton inference algorithm which incorporates three concepts-tree derivatives, grammatical expansion, and inferential strength. Tree derivatives are used for comparing tree forms. Grammatical expansion is a feedback mechanism which effectively enlarges the user sample. An inferential strength parameter is input by the user to indicate the amount of support required from the sample for inferences. The algorithm is also applied for inferring finite-state machines. Finally, we address an open problem posed by Joshi and Levy by demonstrating the use of our algorithm for the design of programming languages. PMID:21868999
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
Meng Joo Er; Chang Deng
2004-01-01
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
Inferring Network-Wide Quality in P2P Live Streaming Systems
Liu, Yong
1 Inferring Network-Wide Quality in P2P Live Streaming Systems Xiaojun Hei, Yong Liu and Keith W-- This paper explores how to remotely monitor network-wide quality in mesh-pull P2P live streaming systems having developed this methodology, we apply it to a popular P2P live streaming system, namely, PPLive
Object Metrics for Aspect Systems: Limiting Empirical Inference Based on Modularity
Shiu Lun Tsang; Siobhán Clarke; Elisa Baniassad
In empirical comparisons of Aspect-Oriented (AO) to Object- Oriented (OO) systems, system properties such as understandability, maintainability, reusability, and testability have often been inferred from other metrics, such as lines of code, sites of change, and modularity. However, in traditional OO metrics suites such system properties are assessed separately from these measures. We applied OO metrics for comparing an AO
Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos G.
2013-10-01
An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H? tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H? tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.
Inferring the Gibbs state of a small quantum system
Rau, Jochen [Institut fuer Theoretische Physik, Johann Wolfgang Goethe-Universitaet, Max-von-Laue-Strasse 1, D-60438 Frankfurt am Main (Germany)
2011-07-15
Gibbs states are familiar from statistical mechanics, yet their use is not limited to that domain. For instance, they also feature in the maximum entropy reconstruction of quantum states from incomplete measurement data. Outside the macroscopic realm, however, estimating a Gibbs state is a nontrivial inference task, due to two complicating factors: the proper set of relevant observables might not be evident a priori; and whenever data are gathered from a small sample only, the best estimate for the Lagrange parameters is invariably affected by the experimenter's prior bias. I show how the two issues can be tackled with the help of Bayesian model selection and Bayesian interpolation, respectively, and illustrate the use of these Bayesian techniques with a number of simple examples.
NASA Astrophysics Data System (ADS)
Asoodeh, Mojtaba; Bagheripour, Parisa
2012-01-01
Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
Sang Min Oh; James M. Rehg; Tucker R. Balch; Frank Dellaert
2008-01-01
Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for several reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model induced
Reducing Failure Rates of Robotic Systems though Inferred Invariants Monitoring
Farritor, Shane
, Sebastian Elbaum and Carrick Detweiler Abstract-- System monitoring can help to detect abnormal- ities. An application of the technique on a system consisting of a unmanned aerial vehicle (UAV) landing on a moving. INTRODUCTION Monitoring a system for anomalies is a common approach to detect conditions that may lead
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
Parameter and Structure Inference for Nonlinear Dynamical Systems
NASA Astrophysics Data System (ADS)
Morris, Robin D.; Smelyanskiy, Vadim N.; Millonas, Mark
2005-11-01
A great many systems can be modeled in the nonlinear dynamical systems framework, as ? = f(x) + ?(t), where f() is the potential function for the system, and ? is the excitation noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We use the Bayesian Information Criteria (BIC) to rank models, together with the beam search to search the space of models. We show that we can accurately determine the structure of simple nonlinear dynamical system models, and the structure of the coupling between nonlinear dynamical systems where the individual systems are known. This last case has important ecological applications.
Parameter and Structure Inference for Nonlinear Dynamical Systems
NASA Technical Reports Server (NTRS)
Morris, Robin D.; Smelyanskiy, Vadim N.; Millonas, Mark
2006-01-01
A great many systems can be modeled in the non-linear dynamical systems framework, as x = f(x) + xi(t), where f() is the potential function for the system, and xi is the excitation noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications.
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.
A self-generating fuzzy rules inference system for petrophysical properties prediction
C. C. Fung; K. W. Wong; P. M. Wong
1997-01-01
This paper discusses the application of a self-generating fuzzy rule extraction and inference system for the prediction of petrophysical properties from well log data. A set of core data with known characteristics is first selected as the training samples. Fuzzy rules are then extracted and undergo a process of rule elimination. The reduced rule set forms the rule-base of the
Functional equivalence between radial basis function networks and fuzzy inference systems
J.-S. R. Jang; C.-T. Sun
1993-01-01
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe
Incorporation, characterization, and conversion of negative rules into fuzzy inference systems
Jerry S. Branson; John H. Lilly
2001-01-01
This paper considers the incorporation of negative examples into fuzzy inference systems (FIS). A new method of defuzzification called dot attenuation is presented. This is a generalization of conventional defuzzification that has the ability to incorporate negative examples into the FIS reasoning process. Several variations of dot attenuation including dot product attenuation (DPA), dot minimum attenuation, and dot difference attenuation
NASA Technical Reports Server (NTRS)
Harrison, P. Ann
1992-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'
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. PMID:25679611
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.
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.
Asymptotic inference in system identification for the atom maser
Catalin Catana; Merlijn van Horssen; Madalin Guta
2011-12-09
System identification is an integrant part of control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However for quantum dynamical systems like quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input which may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators and the connection to large deviations is briefly discussed.
A Fuzzy Inference System Approach for Knowledge Management Tools Evaluation
F. M. Hamundu; Rahmat Budiarto
2010-01-01
Knowledge management has become a very important role in enterprises business process. Regarding to the effective and efficiency of knowledge management system (KMS) implementation, information technology (IT) or tools to support KMS can be a successful factor. One of the challenges that will be faced by enterprises in KMS implementation is the evaluation and selection an appropriate knowledge management tools
On inferring autonomous system relationships in the internet
Lixin Gao
2001-01-01
The Internet consists of rapidly increasing number of hosts interconnected by constantly evolving networks of links and routers. Interdomain routing in the Internet is coordinated by the Border Gateway Protocol (BGP). BGP allows each autonomous system (AS) to choose its own administrative policy in selecting routes and propagating reachability information to others. These routing policies are constrained by the contractual
Jianxin Zhang; Qihua Xiao; Dongmei Huang; Jianwei Zhang
2009-01-01
It is well known that fuzzy inference system and fuzzy comprehensive evaluation, as important member of computational intelligence, are consistent in the target layer. The paper analyzes the mapping relations of fuzzy comprehensive evaluation, and also proves that nonlinear approximation ability of fuzzy comprehensive evaluation is as good as fuzzy inference system's. In order to acquire better weight, The paper
Methodology to Test and Validate a VHDL Inference Engine through the Xilinx System Generator
José A. Olivas; Roberto Sepúlveda; Oscar Montiel; Oscar Castillo
2008-01-01
There exists an increasing interest in the field of digital intelligent systems, being one of the current research target\\u000a the computational efficiency. This work presents the implementation of a fuzzy inference system faced to achieve high performance\\u000a computations since the highly flexibility that a specific tailored FPGA implementation can offer to parallelize processes.\\u000a A methodology to simulate and validate the
Large-Scale Optimization for Bayesian Inference in Complex Systems
Willcox, Karen [MIT] [MIT; Marzouk, Youssef [MIT] [MIT
2013-11-12
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.
Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology
Murakami, Yohei
2014-01-01
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832
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.
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.
Application of MR damper in structural control using ANFIS method
Zhi Q. Gu; S. Olutunde Oyadiji
2008-01-01
Protection of civil engineering structures from excessive vibration due to uncontrollable events such as earthquake has increasingly been of importance for the purpose of maintaining structural integrity and serviceability. This paper presents the development of an adaptive neuro-fuzzy inference system (ANFIS) controller for reduction of environmentally induced vibration in multiple-degree-of-freedom (MDOF) building structure with MR damper. The LQG control method
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.
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
2009-04-01
This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level and precipitable water. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy in 90 percent of the years
NSDL National Science Digital Library
Mrs. Devitry
2012-03-23
Sometimes someone will try to tell you something without coming right out and saying it. He will imply it. When you understand what is implied, you infer. Sometimes you can infer the truth even when the speaker or writer isn?t trying to be helpful. That?s called ?reading between the lines.? Complete Inference Activities 1-8 with 100% accuracy! 1. Making Inferences or Predictions Lesson 2. Making Inferences or Predictions Practice 3. Making Inferences Lesson 4. Making Inferences Practice 5. Inferring Character Feelings Lesson 6. Inferring Character Feelings Practice 7. Inference Lesson 8. Inference Practice ...
Michelangelo Grigni; Dimitris Papadias; Christos H. Papadimitriou
1995-01-01
Geographical database systems deal with certainbasic topological relations such as "A overlapsB" and "B contains C" between simplyconnected regions in the plane. It is of greatinterest to make sound inferences from elementarystatements of this form. This problem hasbeen identified extensively in the recent literature,but very limited progress has been madetowards addressing the considerable technicaldifficulties involved. In this paper we studythe
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
Network inference of AP pattern formation system in D.melanogaster by structural equation modeling
NASA Astrophysics Data System (ADS)
Aburatani, S.; Toh, H.
2014-03-01
Within the field of systems biology, revealing the control systems functioning during embryogenesis is an important task. To clarify the mechanisms controlling sequential events, the relationships between various factors and the expression of specific genes should be determined. In this study, we applied a method based on Structural Equation Modeling (SEM), combined with factor analysis. SEM can include the latent variables within the constructed model and infer the relationships among the latent and observed variables, as a network model. We improved a method for the construction of initial models for the SEM calculation, and applied our approach to estimate the regulatory network for Antero-Posterior (AP) pattern formation in D. melanogaster embryogenesis. In this new approach, we combined cross-correlation and partial correlation to summarize the temporal information and to extract the direct interactions from the gene expression profiles. In the inferred model, 18 transcription factor genes were regulated by not only the expression of other genes, but also the estimated factors. Since each factor regulated the same type of genes, these factors were considered to be involved in maternal effects or spatial morphogen distributions. The interpretation of the inferred network model allowed us to reveal the regulatory mechanism for the patterning along the head to tail axis in D. melanogaster.
A Neuro-Fuzzy Approach for Medical Image Fusion.
Das, Sudeb; Kundu, Malay K
2013-09-18
This paper addresses a novel approach to the multisensor, multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of non-subsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modeling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of RPCNN with less complex structure and having less number of parameters, leads to computational efficiencyan important requirement of point-of-care (POC) health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details and unwanted image degradations etc. Subjective and objective evaluations show better performance of this new approach compared to existing techniques. PMID:24058012
Optimization of Hybrid Electric Cars by Neuro-Fuzzy Networks
Fabio Massimo Frattale Mascioli; Antonello Rizzi; Massimo Panella; Claudia Bettiol
2007-01-01
In this paper, the problem of the optimization of energetic\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009flows in hybrid electric vehicles is faced. We consider a hybrid electric \\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009vehicle\\u0009equipped with batteries, a thermal engine (or fuel cells), ultracapacitors\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009and an electric engine. The energetic flows are optimized by using a\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009control strategy based on the prediction of short-term and medium-term\\u000a\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009\\u0009vehicle states (energy consumption, vehicle
Simulation of neuro-fuzzy controlled grid interactive inverter
N. Altin; I. Sefa
2011-01-01
In this study, a grid interactive inverter which is capable of importing electrical energy, generated from renewable energy sources such as wind, solar and fuel cells, to the grid line is designed and simulated with MATLAB\\/Simulink. A line frequency transformer and LCL filter is used at the output of the grid interactive inverter that is designed as voltage source inverter.
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.
Immune System Modeling with Infer.NET Vincent Y. F. Tan, John Winn, Angela Simpson, Adnan Custovic
Winn, John
Immune System Modeling with Infer.NET Vincent Y. F. Tan, John Winn, Angela Simpson, Adnan Custovic, UK (MSRC). John Winn (jwinn@microsoft.com) is with MSRC. Angela Simpson and Adnan Custovic ({angela.simpson
NASA Astrophysics Data System (ADS)
Zhang, Daili
Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algo
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
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
Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo
This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.
Free-energy inference from partial work measurements in small systems
Ribezzi-Crivellari, Marco; Ritort, Felix
2014-01-01
Fluctuation relations (FRs) are among the few existing general results in nonequilibrium systems. Their verification requires the measurement of the total work performed on a system. Nevertheless in many cases only a partial measurement of the work is possible. Here we consider FRs in dual-trap optical tweezers where two different forces (one per trap) are measured. With this setup we perform pulling experiments on single molecules by moving one trap relative to the other. We demonstrate that work should be measured using the force exerted by the trap that is moved. The force that is measured in the trap at rest fails to provide the full dissipation in the system, leading to a (incorrect) work definition that does not satisfy the FR. The implications to single-molecule experiments and free-energy measurements are discussed. In the case of symmetric setups a second work definition, based on differential force measurements, is introduced. This definition is best suited to measure free energies as it shows faster convergence of estimators. We discuss measurements using the (incorrect) work definition as an example of partial work measurement. We show how to infer the full work distribution from the partial one via the FR. The inference process does also yield quantitative information, e.g., the hydrodynamic drag on the dumbbell. Results are also obtained for asymmetric dual-trap setups. We suggest that this kind of inference could represent a previously unidentified and general application of FRs to extract information about irreversible processes in small systems. PMID:25099353
Free-energy inference from partial work measurements in small systems.
Ribezzi-Crivellari, Marco; Ritort, Felix
2014-08-19
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
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.
S. A. Ali; C. Cafaro; A. Giffin; D. -H. Kim
2012-02-07
Information geometric techniques and inductive inference methods hold great promise for solving computational problems of interest in classical and quantum physics, especially with regard to complexity characterization of dynamical systems in terms of their probabilistic description on curved statistical manifolds. In this article, we investigate the possibility of describing the macroscopic behavior of complex systems in terms of the underlying statistical structure of their microscopic degrees of freedom by use of statistical inductive inference and information geometry. We review the Maximum Relative Entropy (MrE) formalism and the theoretical structure of the information geometrodynamical approach to chaos (IGAC) on statistical manifolds. Special focus is devoted to the description of the roles played by the sectional curvature, the Jacobi field intensity and the information geometrodynamical entropy (IGE). These quantities serve as powerful information geometric complexity measures of information-constrained dynamics associated with arbitrary chaotic and regular systems defined on the statistical manifold. Finally, the application of such information geometric techniques to several theoretical models are presented.
Strapdown fiber optic gyrocompass using adaptive network-based fuzzy inference system
NASA Astrophysics Data System (ADS)
Li, Qian; Ben, Yueyang; Sun, Feng
2014-01-01
This paper aims to propose a new strapdown fiber optic gyrocompass (strapdown FOGC) using adaptive network-based fuzzy inference system (ANFIS). The strapdown FOGC is based on the principle of strapdown inertial navigation system and utilizes electromagnetic log (EM log) and damping equalizer to bound oscillatory errors existing in attitude and heading. As the introduction of damping technique, extra errors are aroused by EM log errors. To decrease the extra errors, ANFIS is utilized to adjust the damping ratio automatically in terms of the ship maneuver conditions. The simulation and trial results indicate that, compared with the conventional gyrocompass scheme, the proposed one can reduce the attitude and heading errors and improve the system performance effectively.
Another expert system rule inference based on DNA molecule logic gates
NASA Astrophysics Data System (ADS)
WÄ siewicz, Piotr
2013-10-01
With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.
1986-01-01
The modifications to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance are summarized. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of gain update frequency, are also presented.
Using a structural and logics systems approach to infer bHLH-DNA binding specificity determinants.
De Masi, Federico; Grove, Christian A; Vedenko, Anastasia; Alibés, Andreu; Gisselbrecht, Stephen S; Serrano, Luis; Bulyk, Martha L; Walhout, Albertha J M
2011-06-01
Numerous efforts are underway to determine gene regulatory networks that describe physical relationships between transcription factors (TFs) and their target DNA sequences. Members of paralogous TF families typically recognize similar DNA sequences. Knowledge of the molecular determinants of protein-DNA recognition by paralogous TFs is of central importance for understanding how small differences in DNA specificities can dictate target gene selection. Previously, we determined the in vitro DNA binding specificities of 19 Caenorhabditis elegans basic helix-loop-helix (bHLH) dimers using protein binding microarrays. These TFs bind E-box (CANNTG) and E-box-like sequences. Here, we combine these data with logics, bHLH-DNA co-crystal structures and computational modeling to infer which bHLH monomer can interact with which CAN E-box half-site and we identify a critical residue in the protein that dictates this specificity. Validation experiments using mutant bHLH proteins provide support for our inferences. Our study provides insights into the mechanisms of DNA recognition by bHLH dimers as well as a blueprint for system-level studies of the DNA binding determinants of other TF families in different model organisms and humans. PMID:21335608
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.
Zeng, Y; Zhang, J; Yin, H; Pan, Y
2007-01-01
Visual evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). In this paper, an adaptive noise cancellation with neural network-based fuzzy inference system (NNFIS) was used and the NNFIS was carefully designed to model the VEP signal. It is assumed that VEP responses can be modelled by NNFIS with the centres of its membership functions evenly distributed over time. The weights of NNFIS are adaptively determined by minimizing the variance of the error signal using the least mean squares (LMS) algorithm. As the NNFIS is dynamic to any change of VEP, the non-stationary characteristics of VEP can be tracked. Thus, this method should be able to track the VEP. Four sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method. PMID:17454407
An expert system shell for inferring vegetation characteristics: Atmospheric techniques (Task G)
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1993-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoals has been implemented. The subgoal Atmospheric Passes allows the scientist to take reflectance data measured at ground level and predict what the reflectance values would be if the data were measured at a different atmospheric height. The subgoal Atmospheric Corrections allows atmospheric corrections to be made to data collected from an aircraft or by a satellite to determine what the equivalent reflectance values would be if the data were measured at ground level. The report describes the implementation and testing of the basic framework and interface for the Atmospheric Techniques Subgoals.
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.
Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications
NASA Astrophysics Data System (ADS)
Chen, Cheng-Hung; Liao, Yen-Yun
2014-04-01
This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the functional-link-based neurofuzzy inference system (FLNIS) for prediction applications. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. The proposed TPSO uses a self-clustering algorithm to divide the particle swarm into multiple tribes, and selects suitable evolution strategies to update each particle. The TPSO also uses a tribal adaptation mechanism to remove and generate particles and reconstruct tribal links. The tribal adaptation mechanism can improve the qualities of the tribe and the tribe adaptation. Finally, the FLNIS model with the proposed TPSO (FLNIS-TPSO) was used in several predictive applications. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.
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.
NASA Astrophysics Data System (ADS)
Gates, J. B.; Nicot, J.; Scanlon, B. R.
2008-12-01
Arsenic is a prominent trace element in the Gulf Coastal Aquifer System (GCAS) in Texas, particularly in the southwestern portion where 29% of wells exceed the USEPA maximum contaminant level of 10 ?g/L for drinking water. While the dominant source is generally thought to be geogenic rather than anthropogenic, little is known about the hydrologic/geochemical mechanisms affecting occurrence in groundwater. The aim of this study was to assess spatial trends in hydrochemistry on a regional scale to help infer relevant processes. The investigation included geostatistical analysis of water quality results from the Texas Water Development Board groundwater database (n>1000) and chemical/isotopic analysis of a transect (17 wells) in the unconfined portion of the Jasper Aquifer, where some of the highest arsenic concentrations in the GCAS are found. Across the GCAS, arsenic and other oxyanion-forming elements (vanadium, molybdenum etc) are most common in the Miocene-age Jasper Aquifer, and tend to decrease with decreasing aquifer age. Principal Component Analysis suggests that spatial variations in arsenic in the GCAS as a whole are related to both total mineralization (TDS), and a second orthogonal component comprised of several trace elements (most prominently vanadium and silicon). A similar relationship is apparent for the Jasper Aquifer, but without a strong correspondence to TDS. The Jasper Aquifer transect also reflects these patterns. Near-neutral pH and slightly-oxidizing conditions observed in the transect are not likely to promote reductive dissolution or desorption from mineral oxides, and no relationship with pH or Eh is present. Rather, maximum arsenic values in the transect (120 ?g/L) coincide with the boundary of the underlying Catahoula Formation which is a known source of saline fluids. Mixing of upward leakage with meteoric recharge is therefore considered to be a likely mechanism controlling arsenic concentrations. This inference is consistent with other geochemical and hydrogeologic considerations and will help to target further research.
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…
Jianqiang Yi; Naoyoshi Yubazaki; Kaoru Hirota
2001-01-01
A new fuzzy controller is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model for upswing and stabilization control of inverted pendulum system. The fuzzy controller takes the angle and angular velocity of the pendulum and the position and velocity of the cart as its input items, and the driving force as its output item.
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
The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection
NASA Astrophysics Data System (ADS)
Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti
2014-06-01
In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.
Crop parameters estimation by fuzzy inference system using X-band scatterometer data
NASA Astrophysics Data System (ADS)
Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.
2013-03-01
Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.
NASA 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.
2014-01-01
Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm. PMID:24708632
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)
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.
Kanno, Y.; Vokoun, J.C.; Letcher, B.H.
2011-01-01
Brook trout Salvelinus fontinalis populations have declined in much of the native range in eastern North America and populations are typically relegated to small headwater streams in Connecticut, USA. We used sibship reconstruction to infer mating systems, dispersal and effective population size of resident (non-anadromous) brook trout in two headwater stream channel networks in Connecticut. Brook trout were captured via backpack electrofishing using spatially continuous sampling in the two headwaters (channel network lengths of 4.4 and 7.7 km). Eight microsatellite loci were genotyped in a total of 740 individuals (80-140 mm) subsampled in a stratified random design from all 50 m-reaches in which trout were captured. Sibship reconstruction indicated that males and females were both mostly polygamous although single pair matings were also inferred. Breeder sex ratio was inferred to be nearly 1:1. Few large-sized fullsib families (>3 individuals) were inferred and the majority of individuals were inferred to have no fullsibs among those fish genotyped (family size = 1). The median stream channel distance between pairs of individuals belonging to the same large-sized fullsib families (>3 individuals) was 100 m (range: 0-1,850 m) and 250 m (range: 0-2,350 m) in the two study sites, indicating limited dispersal at least for the size class of individuals analyzed. Using a sibship assignment method, the effective population size for the two streams was estimated at 91 (95%CI: 67-123) and 210 (95%CI: 172-259), corresponding to the ratio of effective-to-census population size of 0.06 and 0.12, respectively. Both-sex polygamy, low variation in reproductive success, and a balanced sex ratio may help maintain genetic diversity of brook trout populations with small breeder sizes persisting in headwater channel networks. ?? 2010 Springer Science+Business Media B.V.
BCCS 2008/09: Graphical models and complex stochastic systems: Lecture 2: Statistical inference
Green, Peter
; statistics can be collections of either, or the discipline itself, and even that is a bit ambiguous Â does it include probability? Â does it include artificial intelligence or machine learning? Â does it include all is the fundamental question in the philosophy of science. The discipline of statistical inference arose as an attempt
A fuzzy inference system for modelling streamflow: Case of Letaba River, South Africa
NASA Astrophysics Data System (ADS)
Katambara, Zacharia; Ndiritu, John
Streamflow modelling of Letaba River in South Africa is complicated by several factors including the existence of dams and other storage structures whose releases are intermittent and based on rules of thumb depending on the irrigation demands and the need to maintain the flow required in the Kruger National park (KNP). The KNP is located about a hundred kilometres downstream of the main storage and water flows through an alluvial aquifer where complex surface-groundwater interactions occur. Farmers abstract water intermittently along the route directly from the river or indirectly from the alluvial aquifer complicating the flow patterns even more. Consequently, the streamflow series in the river shows very little similarity to what would be considered as natural. The actual abstractions are not measured and only monthly estimates of the abstractions currently exist. Like in many other basins in South Africa, streamflow, groundwater level, rainfall and evaporation data in Letaba is sparse and not very reliable. The Takagi-Sugeno fuzzy inference system using subtractive clustering, an approach which are capable of dealing with vague and inadequate information and data has therefore been used to develop a daily streamflow model for Letaba River. In order to take into account the spatial variability and to maximize the use of the available data, the model is applied in a semi-distributed manner consisting of three river reaches. The shuffled complex evolution (SCE-UA) optimizer has been used to calibrate the model. Six years of data from March 2002 to April 2008 has been used for model calibration and verification. To maximize the Nash-Sutcliffe efficiency, the minimum number of clusters required was found to be 10 for 1000 data points in calibration. An analysis of the location of the cluster centers, the coefficients relating the inputs with the simulated streamflow, and the degrees of membership indicates that no single cluster can be associated to the simulation of a specific hydrologic process or component of the streamflow hydrograph (e.g. high flows or low flows). The fuzzy model does not therefore provide any evidence that it is not a pure black box. The Nash-Sutcliffe efficiency results obtained in calibration and verification showed average values of 0.658 and 0.535 with poor values on the first river reach. Very low percent bias values averaging to -0.4% and -2.7% in calibration and verification are obtained highlighting the model’s potential for applications where mass balance considerations are most important.
NASA Astrophysics Data System (ADS)
Mahroos, Ashraf M.; Kadah, Yasser M.
2010-03-01
One of the recent themes to study the brain dynamics is studying the effective connectivity between brain regions in the target area. We propose and apply algorithm model, dynamic causal modeling (DCM), Psychophysiological interaction (PPI) and first order kernels and also SVD applied directly to singular intrinsic connectivity matrix end up to integrate and describe the interaction of several Brain Regions based on functional magnetic resonance imaging time series to make inferences about functional integration and segregation within the human brain. The method is to demonstrate using real data to show how such models are able to characterize interregional dependence. We extend estimating and reviewing designed model to characterize the interactions between regions and then to estimate the effective connectivity between these regions. All designs, estimates, reviews are implemented using SPM, one of the free best software packages used for design models and analysis for inferring about FMRI functional magnetic resonance imaging time series.
ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.
Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine
2012-04-01
Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem. PMID:21562791
Practical Static Ownership Inference
Ana Milanova; Yin Liu
There are many proposals for ownership type systems de- signed to control aliasing in object-oriented programs. Most systems re- quire signicant annotation eort and therefore it may be dicult to adopt these systems in software practice. Ownership inference has received less attention, while it is an impor- tant problem because it could ease the annotation eort and facilitate application of
Inductive Inference: Theory and Methods
Dana Angluin; Carl H. Smith
1983-01-01
There has been a great deal of theoretical and experimental work in computer science on inductive inference systems, that is, systems that try to infer general rules from examples. However, a complete and applicable theory of such systems is still a distant goal. This survey highlights and explains the main ideas that have been developed in the study of inductive
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.
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)
Christensen, Claire Petra
Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author's own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject. The review-like Chapters 1, 2, and 4 are intended to convey the co-evolution of network science and the slow but noticeable breakdown of boundaries between disciplines in academia as research and comparison of diverse systems has brought to light the shared properties of these systems. It is the author's hope that theses chapters impart some sense of the remarkable and rapid progress in complex systems research that has led to this unprecedented academic synergy. Chapters 3 and 5 detail the author's original work in the context of complex systems research. Chapter 3 presents the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. These networks are then analyzed from a graph theoretical perspective, and their biological viability is critiqued by comparing the networks' graph theoretical properties to those of other biological systems. The results of topological perturbation analyses revealing commonalities in behavior at multiple levels of complexity are also presented, and are shown to be an invaluable means by which to ascertain the level of complexity to which the network inference process is robust to noise. Chapter 5 outlines a learning algorithm for the development of a realistic, evolving social network (a city) into which a disease is introduced. The results of simulations in populat
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
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.
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.
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.
A B S T R A C T Nowadays, forecasting on what will happen in economic
Paris-Sud XI, Université de
. KEYWORDS: Efficient Market Hypothesis, Financial Forecasting, Chemicals, Artificial Intelligence, Genetic Programming, Decision Support System, Hybrid Neuro Fuzzy Model. 1. INTRODUCTION nnovation of Artificial of applying the new mathematics, time series and even some advanced tools, such as Artificial Intelligence
Fuzzy Control of Stochastic Global Optimization Algorithms and Very ...
Finance , Management , Medicine , etc.. In practical ... suited to applications involving neuro-fuzzy systems and neural ... parameter spaces , when computational resources are scarce. .... controller) that does nothing more than emulate human.
Compatibility and reuse in component-based systems via type and unit inference Christian Kuhnel1
using MAT- LAB/Simulink. In order to facilitate reuse, and to raise the level of abstraction for future, such as automotive and embedded control systems in general. Tool chains used in these domains are often based on MAT systems like adaptive cruise control, engine man- agement, or electronic brake systems, reuse is often con
Whelan, Simon
2008-01-01
Molecular phylogenetics examines how biological sequences evolve and the historical relationships between them. An important aspect of many such studies is the estimation of a phylogenetic tree, which explicitly describes evolutionary relationships between the sequences. This chapter provides an introduction to evolutionary trees and some commonly used inferential methodology, focusing on the assumptions made and how they affect an analysis. Detailed discussion is also provided about some common algorithms used for phylogenetic tree estimation. Finally, there are a few practical guidelines, including how to combine multiple software packages to improve inference, and a comparison between Bayesian and maximum likelihood phylogenetics. PMID:18566770
Bugs as deviant behavior: a general approach to inferring errors in systems code
Dawson Engler; David Yu Chen; Seth Hallem; Andy Chou; Benjamin Chelf
2001-01-01
A major obstacle to finding program errors in a real system is knowing what correctness rules the system must obey. These rules are often undocumented or specified in an ad hoc manner. This paper demonstrates techniques that automatically extract such checking information from the source code itself, rather than the programmer, thereby avoiding the need for a priori knowledge of
Kim, Y J; Bae, H; Ko, J H; Poo, K M; Kim, S; Kim, C W; Woo, H J
2006-01-01
A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable. PMID:16532750
Inferring Network-Wide Quality in P2P Live Streaming Systems
Xiaojun Hei; Yong Liu; Keith W. Ross
2007-01-01
This paper explores how to remotely monitor network-wide quality in mesh-pull P2P live streaming systems. Peers in such systems advertise to each other buffer maps which summarize the chunks of data that they currently have cached and make available for sharing. We show how buffer maps can be exploited to monitor network-wide quality. We show that information provided in a
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)
Villasante-Marcos, Víctor; Finizola, Anthony; Abella, Rafael; Barde-Cabusson, Stéphanie; Blanco, María José; Brenes, Beatriz; Cabrera, Víctor; Casas, Benito; De Agustín, Pablo; Di Gangi, Fabio; Domínguez, Itahiza; García, Olaya; Gomis, Almudena; Guzmán, Juan; Iribarren, Ilazkiñe; Levieux, Guillaume; López, Carmen; Luengo-Oroz, Natividad; Martín, Isidoro; Moreno, Manuel; Meletlidis, Stavros; Morin, Julie; Moure, David; Pereda, Jorge; Ricci, Tullio; Romero, Enrique; Schütze, Claudia; Suski-Ricci, Barbara; Torres, Pedro; Trigo, Patricia
2014-02-01
An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~ 237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.
NASA Astrophysics Data System (ADS)
Villasante-Marcos, Víctor; Finizola, Anthony; Barde-Cabusson, Stéphanie; López, Carmen; Di Gangi, Fabio; Levieux, Guillaume; Morin, Julie; Ricci, Tullio; Schütze, Claudia; Suski-Ricci, Barbara
2014-05-01
An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.
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...
Object Metrics for Aspect Systems: Limiting Empirical Inference Based on Modularity
Baniassad, Elisa
and complex problems is that breaking up the problem into smaller parts enhances understandability the ability to break free of object-oriented decomposition, and describe design with a greater degree that they provide relating to the relationship between modularity and system properties. Section 4 documents some
Identification of cement rotary kiln using hierarchical wavelet fuzzy inference system
A. Sharifi; M. Aliyari Shoorehdeli; M. Teshnehlab
Rotary kiln is the central and the most complex component of cement production process. It is used to convert calcineous raw meal into cement clinkers, which plays a key role in quality and quantity of the final produced cement. This system has complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedure, a large
DESIGN OF A PREDICTION MODEL FOR CEMENT ROTARY KILN USING WAVELET PROJECTION FUZZY INFERENCE SYSTEM
A. Sharifi; M. Aliyari Shoorehdeli; M. Teshnehlab
2012-01-01
In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model
A knowledge-based method for inferring semantic concepts from visual models of system behavior
Kevin L. Mills; Hassan Gomaa
2000-01-01
Software designers use visual models, such as data flow\\/control flow diagrams or object collaboration diagrams, to express system behavior in a form that can be understood easily by users and by pogrammers, and from which designers can generate a software architecture. The research described in this paper is motivated by a desire to provide an automated designer's assistant that can
Bayesian inference algorithm on Raw
Luong, Alda
2004-01-01
This work explores the performance of Raw, a parallel hardware platform developed at MIT, running a Bayesian inference algorithm. Motivation for examining this parallel system is a growing interest in creating a self-learning ...
Jacques Zlotnicki; Jean Paul Toutain; Yoichi Sasai; Egardo Villacorte; Alain Bernard; Frederic Fauquet; Toshiyatsu Nagao
2010-01-01
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
W. E. Holt; N. Chamot-Rooke; X. Le Pichon; A. J. Haines; B. Shen-Tu; J. Ren
2000-01-01
We perform a joint inversion of Quaternary strain rates and 238 Global Positioning System (GPS) velocities in Asia for a self-consistent velocity field. The reference frames for all geodetic velocity observations are determined in our inversion procedure. India (IN) moves relative to Eurasia (EU) about a pole of rotation at (29.78°N, 7.51°E, 0.353° Myr-1), which yields a velocity along the
NASA Astrophysics Data System (ADS)
Hidaka, Hiroshi; Ohta, Yohei; Yoneda, Shigekazu
2003-09-01
The Ba isotopic composition of primitive meteorites is one of the more useful tracers to study nucleosynthetic processes in the early solar system, because the seven stable Ba isotopes consist of p-, r-, and s-process nucleosynthetic components and possible decay products from presently extinct 135Cs. Ba isotopic analyses were performed on acid leachates from five carbonaceous chondrites, Murchison (CM2), Sayama (CM2), Allende (CV3), Isuna (CO3) and Maralinga (CK4), and one basaltic achondrite, Juvinas (Eucrite). The Ba isotopic data of acid treatment fractions in carbonaceous chondrites suggest the presence of independent nucleosynthetic components for s- and r-processes in the solar system. The isotopic patterns from the acid residual fractions of two CM2 meteorites, Murchison and Sayama, show an enrichment of s-process isotopes, which is similar to that of presolar SiC grains. On the other hand, their acid leachates have an excess of r-process components among the Ba isotopes. In many cases of carbonaceous chondrites other than CM meteorites, the excess of r-process isotopes is too small for a detailed discussion about the nucleosynthetic contribution. This may be interpreted as metamorphic destruction of presolar grains in meteorite matrices associated with petrographic-type meteorites. Juvinas shows no isotopic anomalies of Ba, because the isotopic record in the early solar system was reset possibly by magmatic processes in the parent body.
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 from the hydrothermal system delineate a general NW-SE ellipsoid in the northern part of MC and may be related to a suspected NW-SE fault along which seismicity takes place and dikes are believed to intrude triggering volcanic crises. The northern flank of the volcano is mechanically and hydro thermally reactivated during seismic crises and this sector could be subjected to a flank failure.
Inference of hidden structures in complex physical systems by multi-scale clustering
Nussinov, Z; Hu, Dandan; Chakrabarty, S; Sahu, M; Sun, Bo; Mauro, N A; Sahu, K K
2015-01-01
We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a complex system involving many elements into optimally decoupled subsets or communities of such elements. We review a multiresolution variant which is used to ascertain structures at different spatial and temporal scales. Significant patterns are obtained by examining the correlations between different independent solvers. Similar to other combinatorial optimization problems in the NP complexity class, community detection exhibits several phases. Typically, illuminating orders are revealed by choosing parameters that lead to extremal information theory correlations.
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
Ying-Zhe Xia; Yi-Yu Chen; Yan Sheng
2006-01-01
Ying-Zhe Xia, Yi-Yu Chen, and Yan Sheng (2006) Phylogeographic structure of lenok (Brachymystax lenok Pallas) (Salmoninae, Salmonidae) populations in water systems of eastern China, inferred from mitochondrial DNA sequences. Zoological Studies 45(2): 190-200. Salmonid fishes represent a model system for address- ing a wide range of biogeographic, evolutionary, and conservation questions. Studies on the genetic structure and phylogeographic pattern of
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.
Bisimulation-based Approximate Lifted Inference
Sen, Prithviraj; Getoor, Lise
2012-01-01
There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the first-order model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic model to speedup inference. In particular, we describe approximate lifted inference techniques that allow the user to trade off inference accuracy for computational efficiency by using a handful of tunable parameters, while keeping the error bounded. Our algorithms are closely related to the graph-theoretic concept of bisimulation. We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for inference can be improved significantly, with approximate lifted inference providing orders of magnitude speedup over ground inference.
Crustal growth of oceanic island arc inferred from seismic structure of Mariana arc-backarc system
NASA Astrophysics Data System (ADS)
Takahashi, N.; Kodaira, S.; Ito, A.; Klemperer, S. L.; Kaneda, Y.; Suyehiro, K.
2004-12-01
The Izu-Ogasawara-Marina arc (IBM arc) is one of the typical oceanic island arcs and it has developed repeating magmatic arc volcanisms and backarc spreading since Eocene. Because tectonics of the IBM arc is relatively simple and does not include collisions between the arc and a continent, it is one of best targets to research crustal growth. In 2003, wide-angle seismic survey using 106 ocean bottom seismographs had been carried out as a part of Margin program in collaboration between US and Japan in Mariana region. The seismic line runs from a serpentine diaper near the trench to Parece Vela basin through the Mariana arc, the Marina trough and the West Mariana ridge. We present the characteristics of the seismic structure of the Mariana arc-backarc system and discuss the crustal growth process by comparison with a structure of the northern Izu-Ogasawara arc. Main structural characteristics of the Mariana arc-backarc system are (1) variation of the crustal thickness (Mariana arc: 20 km, West Mariana ridge: 17 km, Mariana trough and Parece Vela basin: 6 km), (2) distribution of an andesitic middle crust with about P-wave velocity of 6 km/s, (3) variation of P-wave velocity in the middle crust (4) velocity anomalies of the lower crust in transition area between the arc and the backarc, (5) thickening of the lower crust under the Mariana trough axis and (6) slow mantle velocities under the Mariana arc, Mariana trough axis and the West Mariana ridge. Above characteristics from (1) to (4) are common to the seismic structure of the northern Izu-Ogasawara arc. In particular, the vertical P-wave velocity gradients of the middle crust under the forearc in both regions tend to become large rather than those under the arc. Main differences of seismic structures between both regions are the velocity gradients and an existence of a thin transition layer between the middle and lower crust. These differences and similarities of the velocity gradient might originate the age and indicate a difference of a crustal differentiation relating each tectonic stage.
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.
Asteroseismic inference on rotation, gyrochronology and planetary system dynamics of 16 Cygni
NASA Astrophysics Data System (ADS)
Davies, G. R.; Chaplin, W. J.; Farr, W. M.; García, R. A.; Lund, M. N.; Mathis, S.; Metcalfe, T. S.; Appourchaux, T.; Basu, S.; Benomar, O.; Campante, T. L.; Ceillier, T.; Elsworth, Y.; Handberg, R.; Salabert, D.; Stello, D.
2015-01-01
The solar analogues 16 Cyg A and B are excellent asteroseismic targets in the Kepler field of view and together with a red dwarf and a Jovian planet form an interesting system. For these more evolved Sun-like stars we cannot detect surface rotation with the current Kepler data but instead use the technique of asteroseimology to determine rotational properties of both 16 Cyg A and B. We find the rotation periods to be 23.8^{+1.5}_{-1.8} and 23.2^{+11.5}_{-3.2} d, and the angles of inclination to be 56^{+6}_{-5}° and 36^{+17}_{-7}°, for A and B, respectively. Together with these results we use the published mass and age to suggest that, under the assumption of a solar-like rotation profile, 16 Cyg A could be used when calibrating gyrochronology relations. In addition, we discuss the known 16 Cyg B star-planet eccentricity and measured low obliquity which is consistent with Kozai cycling and tidal theory.
Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems
Ghattas, Omar [The University of Texas at Austin] [The University of Texas at Austin
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.
Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems
Sin?ak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vir?ikova, Maria; Vranayova, Zuzana; Sabol, Jakub
2014-01-01
Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475
Artificial intelligence in public health prevention of legionelosis in drinking water systems.
Sin?ak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vir?ikova, Maria; Vranayova, Zuzana; Sabol, Jakub
2014-08-01
Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475
PANFIS: a novel incremental learning machine.
Pratama, Mahardhika; Anavatti, Sreenatha G; Angelov, Plamen P; Lughofer, Edwin
2014-01-01
Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity. PMID:24806644
Type Inference Relevance to Telescoping Languages
Chauhan, Arun
Computing COMP 612: Type Inference December 6, 2002 #12;Towards High-Level Systems for High-Performance-Level Systems for High-Performance Computing Â· software engineering issues - bigger, more complicated languages community? COMP 612: Type Inference December 6, 2002 #12;Towards High-Level Systems for High-Performance
NASA Astrophysics Data System (ADS)
Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad
2014-03-01
Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.
NASA Astrophysics Data System (ADS)
Ansari, Hamid Reza
2014-09-01
In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ?-support vector regression (?-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.
Kenneth Murray
2011-01-01
Purpose – The purpose of this paper is to assert that the exclusive use of predicate offence as a means of proving money laundering is an inadequate response to the level of threat presented by the crime. It aims to promote the concept of “irresistible inference” from UK case law as a basis for establishing international consensus that this provides
David B. Goldstein; Gary W. Roemer; Deborah A. Smith; David E. Reich; Aviv Bergman; Robert K. Wayne
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 microsatel- lite variation at a moderate number of loci (19) can
Neuro-fuzzy control of a robotic exoskeleton with EMG signals
Kazuo Kiguchi; Takakazu Tanaka; Toshio Fukuda
2004-01-01
We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however,
A Neuro-fuzzy based Unified Power Flow Controller for Improvement of Transient Stability Performance
P K Hota
2004-01-01
In this paper an adaptive unified power flow controller (UPFC) has been designed with the application of the intelligent techniques such as a combination of neural network and fuzzy logic. The Mumdani type fuzzy controller with constant center for output classes may not perform satisfactorily in all operating conditions. To overcome this limitation it is made variable according to operating
Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed
Philip D. Olivier
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
A neuro-fuzzy controller for mobile robot navigation and multirobot convoying
Kim C. Ng; Mohan M. Trivedi
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 com- prises of three distinct parts: 1) Fuzzy logic Membership Func- tions (FMF), 2) a Rule Neural Network (RNN), and 3) an Output-Refinement Neural Network (ORNN). FMF are utilized
A Neuro-Fuzzy Approach to Hybrid Intelligent B. Lazzerini, L.M. Reyneri, M. Chiaberge
Reyneri, Leonardo
, Genetic Algorithms, etc. The di erent control strategies are also integrated with Finite State Automata, and the theory of Fuzzy State Automata is derived from that. The novelty of the proposed approach resides state automata 5 , and nally the new the- ory of Fuzzy State Automata 6 , 7 . An immediate synergy can
Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators
Srinivasan Alavandar; M. J. Nigam
2008-01-01
Obtaining the joint variables that result in a desired position of the robot end-effector called as inverse kinematics is one of the most important problems in robot kinematics and control. As the complexity of robot increases, obtaining the inverse kinematics solution requires the solution of non linear equations having tran- scendental functions are difficult and computationally expensive. In this paper,
Neuro-fuzzy controller in biomass-based electric power plant
Francisco Jurado; A. Lopez; J. R. Saenz
2001-01-01
Biomass gasification is a technology that transforms solid biomass into gas. The gas motor controller regulates both the gas motor and the gas motor generator. In this paper two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the
Intelligent Speed Adaptation Using a Self-Organizing Neuro-Fuzzy Controller
Paris-Sud XI, Université de
, car following, and overtaking. This paper presents a new approach to autonomously adapt the speed Organization, estimating that 1.2 millions people are killed in road crashes each year, and as many as 50 years. The global cost of road crashed and injuries is estimated to be US$ 518 billions per year
Bayram Cetisli; Atalay Barkana
2010-01-01
The aim of this study is to speed up the scaled conjugate gradient (SCG) algorithm by shortening the training time per iteration.\\u000a The SCG algorithm, which is a supervised learning algorithm for network-based methods, is generally used to solve large-scale\\u000a problems. It is well known that SCG computes the second-order information from the two first-order gradients of the parameters\\u000a by
Neuro-fuzzy model of superelastic shape memory alloys with application to seismic engineering
Ozbulut, Osman Eser
2009-05-15
................................................................. 61 6.2.3. Earthquake Excitation................................................................. 64 6.3. Dynamic Analysis of a Three Story Benchmark Building................. 67 6.3.1. Optimization of SMA Bracing Elements.... Ranges of strain and strain rate.........................................................................50 Table 5. Maximum response of the frames to the scaled Kobe earthquake record.........66 Table 6. Dynamic characteristic of benchmark building...
Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback
Schurter, Kyle Christopher
2000-01-01
simulations during which the building models are subjected to five different earthquake signals. Signal noise and time delays are found to have only a small effect on operation of the fuzzy controllers. For both structures, performance of the semi...
Simon, Dan
cardiac arrhythmia 2.5 million people/year (US only). ·During AF, electrical impulses originate from many. TABLE 2 ECG parameters Data & Methods AF Control gender male 28 32 female 15 23 age (years) average 70 intervals (SDNN) ms2 Root Mean Squared Differences between Adjacent NN intervals (RMSSD) ms2 frequency Total
Application of neuro-fuzzy techniques in oil pipeline ultrasonic nondestructive testing
Hossein Ravanbod
2005-01-01
This paper presents a novel approach to the problem of nondestructive pipeline testing using ultrasonic imaging. The identification of the flaw type and its dimensions are the most important problems in the pipeline inspection. Unlike typical methods, a decision based neural network is used for the detection of flaws. We train a generalized regression neural network to determine the dimensions
Ajoy Kumar Palit; D. Popovic
2000-01-01
In the actual practice, it becomes interesting from the efficiency point of view to combine various forecasts of a specific time series into a single forecast and to interrogate the resulting forecasting accuracy. The combination is usually nonlinear. Various intelligent combination techniques have been suggested for this purpose, based on different neural network architectures, including the feedforward neural network and
Neuro-fuzzy based Motion Control of a Robotic Exoskeleton: Considering End-effector Force Vectors
Kazuo Kiguchi; Mohammad Habibur Rahman; Makoto Sasaki
2006-01-01
To assist physically disabled, injured, and\\/or elderly persons, we have been developing a 3DOF exoskeleton robot for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. The exoskeleton robot is mainly is controlled by the skin surface electromyogram (EMG) signals, since EMG signals of muscles directly reflect how the user intends to move.
Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting
NASA Astrophysics Data System (ADS)
Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon
In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach
ERIC Educational Resources Information Center
Fazlollahtabar, Hamed; Mahdavi, Iraj
2009-01-01
Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…
NASA Astrophysics Data System (ADS)
Zhang, Y. S.; Wang, H.; Chen, G. L.; Zhang, X. Q.
2007-03-01
Advanced high strength steels are being increasingly used in the automotive industry to reduce weight and improve fuel economy. However, due to increased physical properties and chemistry of high strength steels, it is difficult to directly substitute these materials into production processes currently designed for mild steels. New process parameters and process-related issues must be developed and understood for high strength steels. Among all issues, endurance of the electrode cap is the most important. In this paper, electrode wear characteristics of hot-dipped galvanized dual-phase (DP600) steels and the effect on weld quality are firstly analysed. An electrode displacement curve which can monitor electrode wear was measured by a developing experimental system using a servo gun. A neuro-fuzzy inference system based on the electrode displacement curve is developed for minimizing the effect of a worn electrode on weld quality by adaptively adjusting input variables based on the measured electrode displacement curve when electrode wear occurs. A modified current curve is implemented to reduce the effects of electrode wear on weld quality using a developed neuro-fuzzy system.
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.; ?ahin, Arzu ?encan
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.
Roberto Sepúlveda; Oscar Montiel; José Angel Olivas; Oscar Castillo
2009-01-01
In this paper an improved high performance type-1 inference engine (IE) is proposed that can be applied with no modifications\\u000a to the implementation of a type-2 FIS using the average method. The performance of the type-2 FIS will not be diminish for\\u000a the use of this stage since it is achieved in parallel. The proposals are focused to be implemented
Approximate Decentralized Bayesian Inference
Campbell, Trevor David
This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents. The method first employs variational inference on each ...
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…
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
Application of Transformations in Parametric Inference
ERIC Educational Resources Information Center
Brownstein, Naomi; Pensky, Marianna
2008-01-01
The objective of the present paper is to provide a simple approach to statistical inference using the method of transformations of variables. We demonstrate performance of this powerful tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.…
Inferring Hierarchical Pronunciation Rules from a Phonetic Dictionary
Bogliolo, Alessandro
Inferring Hierarchical Pronunciation Rules from a Phonetic Dictionary Erika Pigliapoco, Valerio Freschi, and Alessandro Bogliolo Abstract--This work presents a new phonetic transcription system based. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context
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.
St. Onge, K. R.; Palmé, A. E.; Wright, S. I.; Lascoux, M.
2012-01-01
Most species have at least some level of genetic structure. Recent simulation studies have shown that it is important to consider population structure when sampling individuals to infer past population history. The relevance of the results of these computer simulations for empirical studies, however, remains unclear. In the present study, we use DNA sequence datasets collected from two closely related species with very different histories, the selfing species Capsella rubella and its outcrossing relative C. grandiflora, to assess the impact of different sampling strategies on summary statistics and the inference of historical demography. Sampling strategy did not strongly influence the mean values of Tajima’s D in either species, but it had some impact on the variance. The general conclusions about demographic history were comparable across sampling schemes even when resampled data were analyzed with approximate Bayesian computation (ABC). We used simulations to explore the effects of sampling scheme under different demographic models. We conclude that when sequences from modest numbers of loci (<60) are analyzed, the sampling strategy is generally of limited importance. The same is true under intermediate or high levels of gene flow (4Nm > 2–10) in models in which global expansion is combined with either local expansion or hierarchical population structure. Although we observe a less severe effect of sampling than predicted under some earlier simulation models, our results should not be seen as an encouragement to neglect this issue. In general, a good coverage of the natural range, both within and between populations, will be needed to obtain a reliable reconstruction of a species’s demographic history, and in fact, the effect of sampling scheme on polymorphism patterns may itself provide important information about demographic history. PMID:22870403
NASA Astrophysics Data System (ADS)
Kadkhodaie-Ilkhchi, Ali; Rahimpour-Bonab, Hossain; Rezaee, Mohammadreza
2009-03-01
Total organic carbon (TOC) content present in reservoir rocks is one of the important parameters, which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon-bearing units. In general, organic-rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher ?-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into 87 training sets to build the CMIS model and 37 testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.
Inference on Counterfactual Distributions
Chernozhukov, Victor V.
Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based ...
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
A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network
Faihan D. Alotaibi; Adel Abdennour; Adel Ahmed Ali
2009-01-01
Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference
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.
Huang, Ting; Wang, Jingjing; Yu, Weichuan; He, Zengyou
2012-09-01
Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Due to the existence of degenerate peptides and 'one-hit wonders', it is very difficult to determine which proteins are present in the sample. In this paper, we review existing protein inference methods and classify them according to the source of peptide identifications and the principle of algorithms. It is hoped that the readers will gain a good understanding of the current development in this field after reading this review and come up with new protein inference algorithms. PMID:22373723
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.
A Knowledge-Based Method for Inferring Semantic Concepts from Graphical Models of Real-time Systems
KEVIN L. MILLS; HASSAN GOMAA
1998-01-01
Designers of software for real-time systems often use models, in the form of data flow\\/control flow diagrams, to express system behavior in a graphical notation that can be understood easily by users and by programmers, and from which designers can generate a software architecture. The research described in this paper is motivated by the desire to provide an automated designer's
Eugene V Koonin
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
Ketland, Jeffrey
2005-01-01
The following inference is valid: There are exactly 101 dalmatians, There are exactly 100 food bowls, Each dalmatian uses exactly one food bowl Hence, at least two dalmatians use the same food bowl. Here, ...
Levine, Daniel S., Ph. D. Massachusetts Institute of Technology
2014-01-01
In resource-constrained inferential settings, uncertainty can be efficiently minimized with respect to a resource budget by incorporating the most informative subset of observations - a problem known as active inference. ...
NASA Astrophysics Data System (ADS)
Murray, Jessica R.; Minson, Sarah E.; Svarc, Jerry L.
2014-07-01
Fault creep, depending on its rate and spatial extent, is thought to reduce earthquake hazard by releasing tectonic strain aseismically. We use Bayesian inversion and a newly expanded GPS data set to infer the deep slip rates below assigned locking depths on the San Andreas, Maacama, and Bartlett Springs Faults of Northern California and, for the latter two, the spatially variable interseismic creep rate above the locking depth. We estimate deep slip rates of 21.5 ± 0.5, 13.1 ± 0.8, and 7.5 ± 0.7 mm/yr below 16 km, 9 km, and 13 km on the San Andreas, Maacama, and Bartlett Springs Faults, respectively. We infer that on average the Bartlett Springs fault creeps from the Earth's surface to 13 km depth, and below 5 km the creep rate approaches the deep slip rate. This implies that microseismicity may extend below the locking depth; however, we cannot rule out the presence of locked patches in the seismogenic zone that could generate moderate earthquakes. Our estimated Maacama creep rate, while comparable to the inferred deep slip rate at the Earth's surface, decreases with depth, implying a slip deficit exists. The Maacama deep slip rate estimate, 13.1 mm/yr, exceeds long-term geologic slip rate estimates, perhaps due to distributed off-fault strain or the presence of multiple active fault strands. While our creep rate estimates are relatively insensitive to choice of model locking depth, insufficient independent information regarding locking depths is a source of epistemic uncertainty that impacts deep slip rate estimates.
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 system, and viruses with the bacterial system never took off. I further suggest that the two other systems of DNA replication, the rolling circle mechanism and the protein-primed mechanism, which are represented in diverse selfish elements, also evolved prior to the emergence of the bacterial replication system. This hypothesis is compatible with the distinct structural affinities of PolB, which has the palm-domain fold shared with reverse transcriptases and RNA-dependent RNA polymerases, and PolC that has a distinct, unrelated nucleotidyltransferase fold. I propose that PolB is a descendant of polymerases that were involved in the replication of genetic elements in the RNA-protein world, prior to the emergence of DNA replication. By contrast, PolC might have evolved from an ancient non-templated polymerase, e.g., polyA polymerase. The proposed temporal succession of the evolving DNA replication systems does not depend on the specific scenario adopted for the evolution of cells and viruses, i.e., whether viruses are derived from cells or virus-like elements are thought to originate from a primordial gene pool. However, arguments are presented in favor of the latter scenario as the most parsimonious explanation of the evolution of DNA replication systems. Conclusion Comparative analysis of the diversity of genomic strategies and organizations of viruses and cellular life forms has the potential to open windows into the deep past of life's evolution, especially, with the regard to the origin of genome replication systems. When complemented with information on the evolution of the relevant protein folds, this comparative approach can yield credible scenarios for very early steps of evolution that otherwise appear to be out of reach. Reviewers Eric Bapteste, Patrick Forterre, and Mark Ragan. PMID:17176463
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 ?uorescent 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
L. Navarro; M. Delgado; J. Urresty; J. Cusido?; L. Romeral
2010-01-01
The present work shows a condition monitoring system applied to electric motors ball bearings. Unlike most of the previous work on this area, which is mainly focused on the location of single-point defects in bearing components - inner and outer races, cage or ball faults -, this research covers wide range irregularities which are very often more difficult to analyse.
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...
Papadopoulos, Evangelos
of widely differing size. That is, animals will tend to transition from a walk to a trot, and from a trot the performance of a legged robot of a similarly configured system, but scaled to a smaller or larger size?" Our work attempts to answer this ques- tion and set the basis for a systematic approach in sizing legged
Robert C. Jachens; Carl M. Wentworth; Mary Lou Zoback; Terry R. Bruns; Carter W. Roberts
Modern high-resolution aeromagnetic surveys over the San Andreas Fault system in the San Francisco Bay region provide detailed information about the positions, shapes, and offset histories of various fault segments concealed beneath water or young alluvium. The presence of coherent, nondis- rupted magnetic rock bodies within the top few kilometers of the crust beneath San Pablo Bay and spanning the
M. R. Rezaee; A. Kadkhodaie Ilkhchi; A. Barabadi
2007-01-01
Shear wave velocity (Vs) associated with compressional wave velocity (Vp) can provide accurate data for geophysical study of a reservoir. These so called petroacoustic studies have important role in reservoir characterization objectives such as lithology determination, identifying pore fluid type, and geophysical interpretation. In this study, fuzzy logic, neuro-fuzzy and artificial neural network approaches were used as intelligent tools to
CUE PROBABILISM AND INFERENCE BEHAVIOR
Lee Roy Beach
1964-01-01
This is an investigation of how Ss use probabilistic cues to make inferences about objects' (Os') class memberships. Os having specified probabilistic cue properties were presented and Ss' inferences were compared to the theoretical predictions. It was concluded that: (a) for unfamiliar Os cue probabilism is important in class inferences; (b) for familiar Os inferences are based upon recognition; (c)
NASA Astrophysics Data System (ADS)
Pedersen, R.; Sigmundsson, F.
2002-12-01
We present measurements of volcano deformation from a series of 18 interferograms spanning the years 1993-2000. The detected deformation originates from repeated intrusions in the Eyjafjallaj”kull system, an icecap covered stratovolcano situated in, what is considered to be, a propagating rift zone in southern Iceland. The volcano erupts infrequently, with only two known eruptions in historic time (last 1100 years). The eruptive products are alkaline in composition, with only small volumes produced in recent eruptions. In spite of the apparent silence of this system two intrusive episodes have been detected within the last decade, causing major concern in the local community. In 1994, and again in 1999, seismic unrest associated with magmatic intrusions occurred in the system. Crustal deformation associated with the events was detected by dry-tilt, GPS and interferometry. During the 1994 episode, the center of deformation was situated underneath the icecap, and the area experiencing maximum uplift was therefore within the zone of decorrelation. The deformation shows an oval fringe pattern, which reaches well beyond the icecap, covering more than 300 km2 in total. Up to 15 cm of LOS ("line of sight") displacement is observed. The temporal resolution of the InSAR images during the 1999 intrusive episode is better and it is possible to follow the development of the intrusive event through time. The center of deformation does not coincide with the center from the 1994 event, but is situated just south of the icecap. The deformation during this event amounts to about 20 cm of LOS. Several of the interferograms cover the whole time-span of the 1999 intrusion, but three interferograms cover different periods of the intrusive event. The data set enables us to follow the temporal development of the crustal deformation created by the intrusion, and hence the growth of the intrusion itself through time. A previous study based on forward modeling of GPS and tilt data suggests similar models for the two events with a Mogi point-source at 3.5 km depth, approximately in the same location 4 km south of the summit crater. The patterns of deformation seen in the interferogram series suggest that this is an oversimplification, as it is evident that the two events had two distinctly separate centers of maximum uplift. We model the deformation created by magma migration through time, by applying a two-step inversion method, in an attempt to generate an image of the magmatic plumbing system within the Eyjafjallaj”kull volcanic system. Relations between the areas of elevated seismicity at the volcano, and the derived plumbing system will be of great importance for improving future hazard assessment.
NASA Astrophysics Data System (ADS)
Li, Bin; Atakan, Kuvvet; Sørensen, Mathilde Bøttger; Havskov, Jens
2015-05-01
Earthquake focal mechanisms of the Shanxi rift system, North China, are investigated for the time period 1965-April 2014. A total of 143 focal mechanisms of ML ? 3.0 earthquakes were compiled. Among them, 105 solutions are newly determined in this study by combining the P-wave first motions and full waveform inversion, and 38 solutions are from available published data. Stress tensor inversion was then performed based on the new database. The results show that most solutions in the Shanxi rift system exhibit normal or strike-slip faulting, and the regional stress field is transtensional and dominated by NNW-SSE extension. This correlates well with results from GPS data, geological field observations and levelling measurements across the faults. Heterogeneity exists in the regional stress field, as indicated by individual stress tensor inversions conducted for five subzones. While the minimum stress axis (?3) appears to be consistent and stable, the orientations, especially the plunges, of the maximum and intermediate stresses (?1 and ?2) vary significantly along the strike of the different subzones. Based on our results and combining multidisciplinary observations from geological surveys, GPS and cross-fault monitoring, a kinematic model is proposed for the Shanxi rift system, in which the rift is situated between two opposite rotating crustal blocks, exhibiting a transtensional stress regimes. This model illustrates the present-day stress field and its correlation to the regional tectonics, as well as the current crustal deformation of the Shanxi rift system. Results obtained in this study, may help to understand the geodynamics, neotectonic activity, active seismicity and potential seismic hazard in this region.
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.
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
Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.
2011-01-01
The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.
NASA Astrophysics Data System (ADS)
Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal
2014-06-01
This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.
Forecasting daily lake levels using artificial intelligence approaches
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher
2012-04-01
Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.
An enhanced segmentation of blood vessels in retinal images using contourlet.
Rezatofighi, S H; Roodaki, A; Ahmadi Noubari, H
2008-01-01
Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms. PMID:19163470
Application of ANN and ANFIS models for reconstructing missing flow data.
Dastorani, Mohammad T; Moghadamnia, Alireza; Piri, Jamshid; Rico-Ramirez, Miguel
2010-07-01
Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches. PMID:19543999
Estimating discharge coefficient of semi-elliptical side weir using ANFIS
NASA Astrophysics Data System (ADS)
Dursun, O. Faruk; Kaya, Nihat; Firat, Mahmut
2012-03-01
SummaryA labyrinth weir is defined as a weir crest that is not straight in planform. The increased sill length provided by the semi-elliptical labyrinth side weirs effectively reduces upstream head to the particular discharge. They can therefore be used to particular advantage where the width of a channel is restricted and a weir is required to pass a range of discharges with a limited variation in upstream water level. In this study, the discharge capacity of semi-elliptical side weirs is estimated by using Adaptive-Neuro Fuzzy Inference System (ANFIS). 675 Laboratory test results are used for determining discharge coefficient of semi-elliptical labyrinth side weirs. The performance of the ANFIS model is compared Multiple Linear Regression (MLR) and Nonlinear Regression (NLR) models based on performance evaluation parameters. Comparison results indicated that the ANFIS technique could be successfully employed in modeling discharge coefficient.
Silver, B.
1986-01-01
This book addresses two questions of great importance to Artificial Intelligence: - how can search be controlled in domains with a large search space. -how can this control information be learned. It is argued that both problems can be tackled with the aid of a technique called meta-level inference. In programs that use meta-level inference the control information is separated from the factual information. The control information is expressed declaratively, i.e. it is represented as explicit rules. These rules are axioms in the meta-theory of the domain. This gives rise to a two-level program: the factual information forms the object-level and the control information forms the meta-level. Inference is performed at the meta-level, and this induces inference at the object-level. Search at the object level is replaced by search at the meta-level. Two Prolog programs, which use this technique, are presented in the book to demonstrate the utility of meta-level inference.
"Groundwater ages" of the Lake Chad multi-layer aquifers system inferred from 14C and 36Cl data
NASA Astrophysics Data System (ADS)
Bouchez, Camille; Deschamps, Pierre; Goncalves, Julio; Hamelin, Bruno; Seidel, Jean-Luc; Doumnang, Jean-Claude
2014-05-01
Assessment of recharge, paleo-recharge and groundwater residence time of aquifer systems of the Sahel is pivotal for a sustainable management of this vulnerable resource. Due to its stratified aquifer system, the Lake Chad Basin (LCB) offers the opportunity to assess recharge processes over time and to link climate and hydrology in the Sahel. Located in north-central Africa at the fringe between the Sahel and the Sahara, the lake Chad basin (LCB) is an endorheic basin of 2,5.106 km2. With a monsoon climate, the majority of the rainfall occurs in the southern one third of the basin, the Chari/Logone River system transporting about 90% of the runoff generated within the drainage basin. A complex multi-layer aquifer system is located in the central part of the LCB. The Quaternary unconfined aquifer, covering 500 000 km2, is characterized by the occurrence of poorly understood piezometric depressions. Artesian groundwaters are found in the Plio-Pleistocene lacustrine and deltaic sedimentary aquifers (early Pliocene and Continental Terminal). The present-day lake is in hydraulic contact with the Quaternary Aquifer, but during past megalake phases, most of the Quaternary aquifer was submerged and may experience major recharge events. To identify active recharge area and assess groundwater dynamics, one hundred surface and groundwater samples of all layers have been collected over the southern part of the LCB. Major and trace elements have been analyzed. Measurements of 36Cl have been carried out at CEREGE, on the French 5 MV AMS National Facility ASTER and 14C activities have been analyzed for 17 samples on the French AMS ARTEMIS. Additionally, the stable isotopic composition was measured on the artesian aquifer samples. In the Quaternary aquifer, results show a large scatter with waters having very different isotopic and geochemical signature. In its southern part and in the vicinity of the surface waters, groundwaters are predominantly Ca-Mg-HCO3 type waters with very high 36Cl/Cl ratio (>1000.10-15 at/at) very likely linked to the bomb pulse. These high 36Cl/Cl ratios are in the same order than the 36Cl/Cl signature of surface waters active modern recharge in this area. In the other part of the Quaternary Aquifer, waters are Na-HCO3-SO4-Cl type and are characterized by lower 36Cl/Cl ratios (around 200.10-15 at/at), suggesting longer residence time of the groundwaters. The 14C contents of the unconfined aquifer waters are all above 50 pmc, suggesting recent or Holocene recharge of this system. In contrast, the confined aquifer has a more homogeneous geochemical signature. The 14C contents are below all 0.5 pmc and mainly below detection level. 36Cl/Cl ratios are
Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang
2014-01-01
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109
Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang
2014-01-01
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN. PMID:25423109
NASA Astrophysics Data System (ADS)
Hurwitz, Shaul; Lowenstern, Jacob B.; Heasler, Henry
2007-05-01
We present and analyze a chemical dataset that includes the concentrations and fluxes of HCO 3-, SO 42-, 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, HCO 3- > Cl - > SO 42- > 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 HCO 3-/Cl -, and SO 42-/Cl - increase during the late-spring high-flow period. HCO 3-/SO 42- 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 HCO 3-/Cl - and SO 42-/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 HCO 3- flux is larger than the flux of other solutes (HCO 3-/Cl - ? 3), the CO 2 equivalent flux is only ˜ 1% of the estimated emission of magmatic CO 2 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 frequencies with better spatial coverage would be required. Our analysis also suggests that it might be more feasible to detect large-scale heating or cooling of the hydrothermal system by tracking changes in gas and steam flux than by tracking changes in river solute flux.
NASA Astrophysics Data System (ADS)
McCubbin, Francis Michael
Magmatic volatiles, specifically water, fluorine, chlorine and sulfur, play important and diverse roles in silicate melts by controlling many physiochemical processes such as thermal stabilities of minerals and melts, melt density and viscosity, magma eruptive processes, and the formation of hydrothermal fluids that transport economically important metals. Some of these volatiles, perhaps most notably water, likely play a crucial role in the origin of life. Although the terrestrial magmatic volatile budget is well constrained, much remains uncertain about the martian volatile budget. Mars has commonly been referred to as a "volatile-rich" planet, and there is little doubt about the presence of frozen water-ice at the martian poles and abundant Cl and S in rocks, soils and dust. Yet, contradictory information abounds, particularly regarding magmatic water contents and the accepted mantle water budget for Mars. This body of work provides the first studies focused on assessing the volatile budget of martian magmas and exploring the implications of these volatiles on ancient martian igneous and hydrothermal systems. We report, through textural analysis and electron probe microanalysis (EPMA) of minerals in martian meteorites, strong evidence for water, F, and Cl-bearing magmas and strong evidence for both water-rich and chlorine-rich hydrothermal fluids in martian magmatic systems. We collected new secondary ion mass spectrometry (SIMS) data on kaersutite from the Chassigny meteorite, which we use to show that at least some magma source regions on Mars likely have water contents similar to terrestrial values. In order to show that low-OH F-Cl apatite analyses obtained from the Chassigny meteorite are viable compositions (these compositions are rare in terrestrial rocks), low-OH F-Cl apatite was synthesized and characterized by EPMA, single-crystal X-ray diffraction and various nuclear magnetic resonance (NMR) techniques. Finally, the effect of water on the compositional diversity of magmas that can be produced from fractionation of a martian liquid at the base of a thick crust was investigated experimentally. Using a synthetic powder modeled after Humphrey (a picrobasalt analyzed in Gusev Crater, Mars), we verified the possibility of igneous crustal stratification, which does not require large-scale lithologic diversity among rocks on the martian surface.
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.
NASA Astrophysics Data System (ADS)
Levasseur-Regourd, A. C.
Information about the physical properties of dust clouds and regoliths in the solar sys- tem is mainly provided by remote light scattering observations. Laboratory measure- ments, which avoid multiple scattering on gravity packed layers by elaborate levitation techniques, are required to accurately interpret such observations. The feasibility of light scattering measurements under microgravity conditions, both on dust clouds and on aggregating particles has been demonstrated by the PROGRA2 experiment during parabolic flight campaigns and by the CODAG-LSU experiment during a rocket flight. The European ICAPS (Interactions in Cosmic and Atmospheric Particle Systems) project has been selected for the International Space Station, and is now at the end of its phase A at ESA. ICAPS Research Topic 5 is devoted to the optical and morphological properties of aggregates. Key results of the PROGRA2 and CODAG-LSU experiments will be presented. Fu- ture steps in the development of light scattering measurements with ICAPS will be presented, with emphasis on new topics, such as the formation and evolution of ices on submicron- or micron-sized dust particles and on highly porous regoliths.
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)
Iepure, S.; Namiotko, T.; Montanari, A.; Brugiapaglia, E.; Mainiero, M.; Mariani, S.; Fiebig, M.
2012-04-01
Cave water and sediments from an extensive sulfidic, chemioautotrophic subterranean ecosystem in the hypogenic karst complex of Frasassi (northeastern Apennines of Italy) was analysed for modern and fossil ostracode assemblages. 22 extant and 16 extinct ostracode species make of this continental sulphidic ecosystem one of the richest worldwide. Both modern and fossil assemblages show the expected pattern of species diversity after the simulation procedure for taxonomic distinctness, which indicates no major extinction events since the Pleistocene. Extant species display patchy distribution according to habitat heterogeneity within the sulphidic environment. Fossil assemblages from a 3 m thick fluvial deposit trapped near the entrance of the Caverna del Carbone (CDC) at about 30 m above present river level, and a fine sand deposit resting at about the same elevation in Sala Duecento (SDS) within the Grotta Grande del Vento preliminarily dated with OSL at 111±17 ka are being investigated. The former deposit has yet to be dated but it represents probably a normal stratigraphic succession spanning a few tens of kyr, which was deposited when the cave entrance was at the reach of fluvial flooding, potentially recording the transition from the last interglacial Riss-Würm to the glacial Würm. Sediment samples from the SDS site yielded an ostracode assemblage represented by 12 species with a d18O signature of -5‰ and a well-diversified palinoflora assemblage indicating a transitional condition between steppe and temperate forest. The top sediment from the CDC site is characterized by a less diversified ostracode assemblage represented by 8 species, d18O of -3‰, and a poorly diversified palinoflora dominated by herbaceous plants and lesser pines, indicating a colder environment in the early stage of the last glacial. Additional information on the geometric morphometry approach of B-splines method applied to extant and fossil specimens of the hypogean Mixtacandona ostracode was used to identify microevolutionary patterns and environmentally cued variation. Analyses indicate the presence of one morphotype of a new species A of the group Mixtacandona riongessa, and three distinctive morphotypes of a species B of the group M. laisi-chappuisi occurring in stratigraphically distinct fluvial-cave sediments. Apparent difference in the disparity level between these species could be associated with their survival in different environmental conditions. Species A is found nowadays living exclusively in sulphidic cave waters, and was present in the system since at least the end of the last interglacial. The extraordinary high taxonomic and morphological diversity of ostracods reflects in situ evolutionary processes that have occurred under the cumulative effect of high environmental energy availability of subterranean sulphidic ecosystems, heterogeneous environmental conditions, and spatial and temporal isolation.
2011-01-01
Background Main waterfowl migration systems are well understood through ringing activities. However, in mallards (Anas platyrhynchos) ringing studies suggest deviations from general migratory trends and traditions in waterfowl. Furthermore, surprisingly little is known about the population genetic structure of mallards, and studying it may yield insight into the spread of diseases such as Avian Influenza, and in management and conservation of wetlands. The study of evolution of genetic diversity and subsequent partitioning thereof during the last glaciation adds to ongoing discussions on the general evolution of waterfowl populations and flyway evolution. Hypothesised mallard flyways are tested explicitly by analysing mitochondrial mallard DNA from the whole northern hemisphere. Results Phylogenetic analyses confirm two mitochondrial mallard clades. Genetic differentiation within Eurasia and North-America is low, on a continental scale, but large differences occur between these two land masses (FST = 0.51). Half the genetic variance lies within sampling locations, and a negligible portion between currently recognised waterfowl flyways, within Eurasia and North-America. Analysis of molecular variance (AMOVA) at continent scale, incorporating sampling localities as smallest units, also shows the absence of population structure on the flyway level. Finally, demographic modelling by coalescence simulation proposes a split between Eurasia and North-America 43,000 to 74,000 years ago and strong population growth (~100fold) since then and little migration (not statistically different from zero). Conclusions Based on this first complete assessment of the mallard's world-wide population genetic structure we confirm that no more than two mtDNA clades exist. Clade A is characteristic for Eurasia, and clade B for North-America although some representatives of clade A are also found in North-America. We explain this pattern by evaluating competing hypotheses and conclude that a complex mix of historical, recent and anthropogenic factors shaped the current mallard populations. We refute population classification based on flyways proposed by ornithologists and managers, because they seem to have little biological meaning. Our results have implications for wetland management and conservation, with special regard to the release of farmed mallards for hunting, as well as for the possible transmission of Avian Influenza by mallards due to migration. PMID:22093799
Perception as Unconscious Inference
Hatfield, Gary
5 Perception as Unconscious Inference GARY HATFIELD Department of Philosophy, University perception to which I've drawn your attention are objects of study in contemporary perceptual psychology, which considers the perception of size, shape, distance, motion, and color. These phenomenal aspects
NSDL National Science Digital Library
Jessica Fries-Gaither
Different approaches to teaching the reading comprehension strategy of inferring in K-5 classrooms are identified in this article. The article appears in the free, online magazine Beyond Weather and the Water Cycle, which is structured around the essential principles of climate science.
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 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.
Making Inferences Using Graphic Organizers
NSDL National Science Digital Library
2013-03-06
Students will use a graphic organizer to practice the skill of making inferences with the help of a picture book by Chris Van Allsburg. They will then continue to practice the skill with a making inferences worksheet.
Action understanding and active inference.
Friston, Karl; Mattout, Jérémie; Kilner, James
2011-02-01
We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action-observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference. PMID:21327826
NASA Astrophysics Data System (ADS)
Fairhead, J. D.; Green, C. M.; Masterton, S. M.; Guiraud, R.
2013-05-01
The Muglad rift basin of Sudan, is a good example of polyphase rifting, with at least three major phases of basin development. Each phase has resulted in the generation of source rock, reservoir and seal geology with structural traps often closely linked to basement highs. In this paper we investigate on a regional scale the tectonic processes that have contributed to rift basin development. On a regional scale, the evolution of the Africa-wide Mesozoic rift system is intimately linked to relative movements of African sub-plates and to global plate tectonic processes and plate interactions. Changes in plate interactions are observed in the oceanic crust as azimuth changes of fracture zone geometries and by inference have caused significant modifications to both the orientation and magnitude of the motions of the African sub-plates. Such plate motion processes have controlled the polyphase development of the West and Central African Rift System. On the basinal scale, changes of sub-plate motions have resulted in changes in the stress field which have had a clear impact on the deformation and fault geometries of rift basins and on the resulting stratigraphy. The construction of the first unified stratigraphic chart for the West and Central African Rift System shows a close correlation in the timing of the major unconformities with the timing of changes in relative plate motion as observed in the changes of the azimuthal geometry of the oceanic fracture zones in the Central Atlantic. Since similarly timed unconformities exist along the continental margins of Africa and South America, we propose that the causative mechanism is change in relative plate motion which leads to an increase or decrease in the tension on the plate and thus controls the strength or effective elastic thickness, Te, of the crust/plate beneath the margins. This results in a focused change in isostatic response of the margin during short-period changes in relative plate motion; i.e. more tension will mean that loads are not compensated locally resulting in local uplift of the margin.
Posterior Predictive Inference Sudipto Banerjee
Carlin, Bradley P.
Posterior Predictive Inference Sudipto Banerjee sudiptob@biostat.umn.edu University of Minnesota Posterior Predictive Inference Â p. 1/ #12;Posterior Predictive Checks Assume modelling data as y = (y1(T(yrep, ) > T(y, ) | y) = 1 M M t=1 1[T(yt rep, t) > T(y, t)]. Posterior Predictive Inference Â p. 2/ #12;Model
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.
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…
Functional association networks as priors for gene regulatory network inference
Studham, Matthew E.; Nordling, Torbjörn E.M.; Nelander, Sven; Sonnhammer, Erik L. L.
2014-01-01
Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data. Contact: matthew.studham@scilifelab.se Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931976
Matrix Factorization for Transcriptional Regulatory Network Inference
Ochs, Michael F.; Fertig, Elana J.
2013-01-01
Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms where multiple regulation and gene reuse increase dramatically. Non-negative matrix factorization techniques were initially introduced to find non-orthogonal patterns in data, making them ideal techniques for inference in cases of multiple regulation. We review these techniques and their application to TRN analysis. PMID:25364782
Continuity of the Maximum-Entropy Inference
NASA Astrophysics Data System (ADS)
Stephan, Weis
2014-09-01
We study the inverse problem of inferring the state of a finite-level quantum system from expected values of a fixed set of observables, by maximizing a continuous ranking function. We have proved earlier that the maximum-entropy inference can be a discontinuous map from the convex set of expected values to the convex set of states because the image contains states of reduced support, while this map restricts to a smooth parametrization of a Gibbsian family of fully supported states. Here we prove for arbitrary ranking functions that the inference is continuous up to boundary points. This follows from a continuity condition in terms of the openness of the restricted linear map from states to their expected values. The openness condition shows also that ranking functions with a discontinuous inference are typical. Moreover it shows that the inference is continuous in the restriction to any polytope which implies that a discontinuity belongs to the quantum domain of non-commutative observables and that a geodesic closure of a Gibbsian family equals the set of maximum-entropy states. We discuss eight descriptions of the set of maximum-entropy states with proofs of accuracy and an analysis of deviations.
Exploring the Operational Characteristics of Inference
. As a first step towards simulating the complete cellular behavior, computer models of gene interaction Systems K.U.Leuven Heverlee, Belgium kathleen.marchal@biw.kuleuven.be Keywords Gene regulatory network, simulated data, network inference, gene expression data Abstract The development of structure
Inferring Social Networks from Distributed Bluetooth Scanning
by the use of Bluetooth and GPS sensors, which allows to obtain data from each one of the mentioned devicesInferring Social Networks from Distributed Bluetooth Scanning FÃ©lix Manuel Rubio Kongens Lyngby of information, the system has to collect data from mobile devices to perform a social behavior re- search
Nonstandard Inferences in Description Logics: The Story So Far
Franz Baader; Ralf Küsters
Description logics (DLs) are a successful family of logic-based knowledge represen- tation formalisms that can be used to represent the terminological knowledge of an application domain in a structured and formally well-founded way. DL systems pro- vide their users with inference procedures that allow to reason about the represented knowledge. Standard inference problems (such as the subsumption and the instance
Evaluation of an inference network-based retrieval model
Howard R. Turtle
1991-01-01
The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models. The performance of a retrieval system based on the inference network model is evaluated and compared to performance with conventional retrieval models.
NASA Astrophysics Data System (ADS)
Golsanami, Naser; Kadkhodaie-Ilkhchi, Ali; Erfani, Amir
2015-01-01
Capillary pressure curves are important data for reservoir rock typing, analyzing pore throat distribution, determining height above free water level, and reservoir simulation. Laboratory experiments provide accurate data, however they are expensive, time-consuming and discontinuous through the reservoir intervals. The current study focuses on synthesizing artificial capillary pressure (Pc) curves from seismic attributes with the use of artificial intelligent systems including Artificial Neural Networks (ANNs), Fuzzy logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs). The synthetic capillary pressure curves were achieved by estimating pressure values at six mercury saturation points. These points correspond to mercury filled pore volumes of core samples (Hg-saturation) at 5%, 20%, 35%, 65%, 80%, and 90% saturations. To predict the synthetic Pc curve at each saturation point, various FL, ANFIS and ANN models were constructed. The varying neural network models differ in their training algorithm. Based on the performance function, the most accurately functioning models were selected as the final solvers to do the prediction process at each of the above-mentioned mercury saturation points. The constructed models were then tested at six depth points of the studied well which were already unforeseen by the models. The results show that the Fuzzy logic and neuro-fuzzy models were not capable of making reliable estimations, while the predictions from the ANN models were satisfyingly trustworthy. The obtained results showed a good agreement between the laboratory derived and synthetic capillary pressure curves. Finally, a 3D seismic cube was captured for which the required attributes were extracted and the capillary pressure cube was estimated by using the developed models. In the next step, the synthesized Pc cube was compared with the seismic cube and an acceptable correspondence was observed.
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.
Fuzzy adaptive vector control of induction motor drives
Emanuele Cerruto; Alfio Consoli; Angelo Raciti; Antonio Testa
1997-01-01
This paper deals with the design and experimental realization of a model reference adaptive control (MRAC) system for the speed control of indirect field-oriented (IFO) induction motor drives based on using fuzzy laws for the adaptive process and a neuro-fuzzy procedure to optimize the fuzzy rules. Variation of the rotor time constant is also accounted for by performing a fuzzy
An Introduction to Fuzzy State Automata L.M. Reyneri
Reyneri, Leonardo
how neuro-fuzzy systems can be interfaced with traditional Finite State Automata, and then introducesAn Introduction to Fuzzy State Automata L.M. Reyneri Dipartimento di Elettronica - Politecnico di ++39 11 568 4099 Abstract This paper introduces Fuzzy State Automata for control applications
Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling
Athanasios S. Drigas; Katerina Argyri; John Vrettaros
2009-01-01
Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic
Distributed Inference and Query Processing for RFID Tracking and Monitoring
Cao, Zhao; Diao, Yanlei; Shenoy, Prashant
2011-01-01
In this paper, we present the design of a scalable, distributed stream processing system for RFID tracking and monitoring. Since RFID data lacks containment and location information that is key to query processing, we propose to combine location and containment inference with stream query processing in a single architecture, with inference as an enabling mechanism for high-level query processing. We further consider challenges in instantiating such a system in large distributed settings and design techniques for distributed inference and query processing. Our experimental results, using both real-world data and large synthetic traces, demonstrate the accuracy, efficiency, and scalability of our proposed techniques.
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.
ERACER: A Database Approach for Statistical Inference and Data Cleaning
Neville, Jennifer
SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated cleaning for the purpose of maintaining quality in relational databases. Data cleaning (or cleansing it possible to automate the cleansing proc
Science Shorts: Observation Versus Inference
NSDL National Science Digital Library
Craig R. Leager
2008-02-01
When you observe something, how do you know for sure what you are seeing, feeling, smelling, or hearing? Asking students to think critically about their encounters with the natural world will help to strengthen their understanding and application of the science-process skills of observation and inference. In the following lesson, students make observations and inferences of an object and some mystery photos.
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…
Functional Classification Using Phylogenomic Inference
Duncan Brown; Kimmen Sjölander
2006-01-01
hylogenomic inference of protein (or gene) function attempts to address the question, ''What function does this protein perform?'' in an evolutionary context. As originally outlined by Jonathan Eisen (1-3), phylogenomic inference of protein function is a multistep process involving selection of homologs, multiple sequence alignment (MSA), and phylogenetic tree construction; overlaying annotations on the tree topology; discriminating between orthologs and
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
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.
Inference about magnitudes of effects.
Barker, Richard J; Schofield, Matthew R
2008-12-01
In a recent commentary on statistical inference, Batterham and Hopkins advocated an approach to statistical inference centered on expressions of uncertainty in parameters. After criticizing an approach to statistical inference driven by null hypothesis testing, they proposed a method of "magnitude-based" inference and then claimed that this approach is essentially Bayesian but with no prior assumption about the true value of the parameter. In this commentary, after we address the issues raised by Batterham and Hopkins, we show that their method is "approximately" Bayesian and rather than assuming no prior information their approach has a very specific, but hidden, joint prior on parameters. To correctly adopt the type of inference advocated by Batterham and Hopkins, sport scientists need to use fully Bayesian methods of analysis. PMID:19223677
A pallidus-habenula-dopamine pathway signals inferred stimulus values.
Bromberg-Martin, Ethan S; Matsumoto, Masayuki; Hong, Simon; Hikosaka, Okihide
2010-08-01
The reward value of a stimulus can be learned through two distinct mechanisms: reinforcement learning through repeated stimulus-reward pairings and abstract inference based on knowledge of the task at hand. The reinforcement mechanism is often identified with midbrain dopamine neurons. Here we show that a neural pathway controlling the dopamine system does not rely exclusively on either stimulus-reward pairings or abstract inference but instead uses a combination of the two. We trained monkeys to perform a reward-biased saccade task in which the reward values of two saccade targets were related in a systematic manner. Animals used each trial's reward outcome to learn the values of both targets: the target that had been presented and whose reward outcome had been experienced (experienced value) and the target that had not been presented but whose value could be inferred from the reward statistics of the task (inferred value). We then recorded from three populations of reward-coding neurons: substantia nigra dopamine neurons; a major input to dopamine neurons, the lateral habenula; and neurons that project to the lateral habenula, located in the globus pallidus. All three populations encoded both experienced values and inferred values. In some animals, neurons encoded experienced values more strongly than inferred values, and the animals showed behavioral evidence of learning faster from experience than from inference. Our data indicate that the pallidus-habenula-dopamine pathway signals reward values estimated through both experience and inference. PMID:20538770
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.
INFERRING THE ECCENTRICITY DISTRIBUTION
Hogg, David W.; Bovy, Jo [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Myers, Adam D., E-mail: david.hogg@nyu.ed [Max-Planck-Institut fuer Astronomie, Koenigstuhl 17, D-69117 Heidelberg (Germany)
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.
NASA Astrophysics Data System (ADS)
Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Lazarev, Alexander A.
2013-12-01
We consider design and modeling of hardware realizations of reconfigurable multifunctional continuous logic devices (R MCL D) as advanced components of the next generation high-performance MIMO-systems for the processing and interconnection. The R MCL D realize function of two-valued and continuous logics with current inputs and current outputs on the basis of CMOS current mirrors and circuits which realize the limited difference functions. We show advantages of such elements consisting in encoding of variables by the photocurrent levels, that allows easily providing optical inputs (by photo-detectors (PD)) and optical outputs (by LED). The conception of construction of R MCL D consists in the use of a current mirrors realized on 1.5?m technology CMOS transistors. Presence of 55÷65 transistors, 1 PD and 1 LED makes the offered circuits quite compact and allows their integration in 1D and 2D arrays. In the presentation we consider the capabilities of the offered circuits, show the simulation results and possible prospects of application of the circuits in particular for time-pulse coding for multivalued, continuous, neuro-fuzzy and matrix logics. The simulation results of NOT, MIN, MAX, equivalence (EQ) and other functions, that implemented R MCL D, showed that the level of logical variables can change from 1 ?A to 10 ?A for low-power consumption variants. The base cell of the R MCL D have low power consumption <1mW and processing time about 1÷11?S at supply voltage 2.4÷3.3V. Modeling of such cells in OrCad is made.
Computational statistics using the Bayesian Inference Engine
NASA Astrophysics Data System (ADS)
Weinberg, Martin D.
2013-09-01
This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.
Ontological inference for image and video analysis
Christopher Town
2006-01-01
This paper presents an approach to designing and implementing extensible computational models for perceiving systems based on a knowledge-driven joint inference approach. These models can integrate different sources of information both horizontally (multi-modal and temporal fusion) and vertically (bottom–up, top–down) by incorporating prior hierarchical knowledge expressed as an extensible ontology.Two implementations of this approach are presented. The first consists of
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
Bayesian inference of substrate properties from film behavior
NASA Astrophysics Data System (ADS)
Aggarwal, R.; Demkowicz, M. J.; Marzouk, Y. M.
2015-01-01
We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn–Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems.
Bayesian Modeling, Inference and Prediction
Draper, David
, 1984). #12;ii David Draper #12;Bayesian Modeling, Inference and Prediction iii To Andrea, from whom I 4.2 Bayesian model choice . . . . . . . . . . . . . . . . . . . . . . 226 4.2.1 Data-analytic model
Original article Inference about multiplicative
Paris-Sud XI, Université de
for estimating variance components (Patterson and Thompson, 1971): However, heterogeneous variances are oftenOriginal article Inference about multiplicative heteroskedastic components of variance in a mixed for the sire and residual components of variance. heteroskedasticity / mixed linear model / Bayesian technique
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.
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
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
Inferring Epidemic Network Topology from Surveillance Data
Wan, Xiang; Liu, Jiming; Cheung, William K.; Tong, Tiejun
2014-01-01
The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases. PMID:24979215
Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics
2012-01-01
Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identification of the microscopic pharmacokinetics and pharmacodynamics of a drug with a minimal a priori knowledge. In fact, the inference model presented in this paper is completely unsupervised. It takes as input the time series of the concetrations of the parent drug and its metabolites. The method, applied to the case study of the gemcitabine pharmacokinetics, shows good accuracy and sensitivity. PMID:22640931
Making meaningful inferences about magnitudes.
Batterham, Alan M; Hopkins, William G
2006-03-01
A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an inference about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or nonsignificant on the basis of a P value derived from a null-hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. The authors use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First they express the uncertainty as confidence limits, which define the likely range of the true value. They then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some positive and negative sense, such as beneficial or harmful. If the likely range overlaps substantially positive and negative values, they infer that the outcome is unclear; otherwise, they infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. They refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (eg, very likely beneficial). Quantitative or qualitative probabilities that the true value has the other 2 magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome. PMID:19114737
Heddam, Salim
2014-08-01
In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling. PMID:24705953
Inference for interacting linear waves in ordered and random media
Tyagi, P; Antenucci, F; Berganza, M Ibáñez; Leuzzi, L
2015-01-01
A statistical inference method is developed and tested for pairwise interacting systems whose degrees of freedom are continuous angular variables, such as planar spins in magnetic systems or wave phases in optics and acoustics. We investigate systems with both deterministic and quenched disordered couplings on two extreme topologies: complete and sparse graphs. To match further applications in optics also complex couplings and external fields are considered and general inference formulas are derived for real and imaginary parts of Hermitian coupling matrices from real and imaginary parts of complex correlation functions. The whole procedure is, eventually, tested on numerically generated correlation functions and local magnetizations by means of Monte Carlo simulations.
Efficient inference of protein structural ensembles
Lane, Thomas J; Beauchamp, Kyle A; Pande, Vijay S
2014-01-01
It is becoming clear that traditional, single-structure models of proteins are insufficient for understanding their biological function. Here, we outline one method for inferring, from experiments, not only the most common structure a protein adopts (native state), but the entire ensemble of conformations the system can adopt. Such ensemble mod- els are necessary to understand intrinsically disordered proteins, enzyme catalysis, and signaling. We suggest that the most difficult aspect of generating such a model will be finding a small set of configurations to accurately model structural heterogeneity and present one way to overcome this challenge.
An algebra-based method for inferring gene regulatory networks
2014-01-01
Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. Conclusions Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html. PMID:24669835
Functional Classification Using Phylogenomic Inference
Sjölander, Kimmen
, phylogenomic inference of gene function is not often used. Far from it. The majority of novel sequences sequence alignment (MSA), and phylogenetic tree construction; overlaying annotations on the tree topology are assigned a putative function through the use of annotation transfer from the top hits in a database search
Ancestral Inference in Population Genetics
R. C. Griffiths; Simon Tavare
1994-01-01
Mitochondrial DNA sequence variation is now being used to study the history of our species. In this paper we discuss some aspects of estimation and inference that arise in the study of such variability, focusing in particular on the estimation of substitution rates and their use in calibrating estimates of the time since the most recent common ancestor of a
Learning and Inferring Transportation Routines
Lin Liao; Dieter Fox; Henry A. Kautz
2004-01-01
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through the commu- nity. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's mode of transporta- tion or her goal. We apply Rao-Blackwellised particle filters for efficient
The mechanisms of temporal inference
NASA Technical Reports Server (NTRS)
Fox, B. R.; Green, S. R.
1987-01-01
The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.
Focused Inference Romer E. Rosales+
Goldwasser, Shafi
Focused Inference RÂ´omer E. Rosales+ and Tommi S. Jaakkola Computer Science and Artificial Intelligence Lab., MIT. Cambridge, MA 02139 + Computer-Aided Diagnosis and Therapy, Siemens Medical Solutions, with the singular exception of [18], do not pro- vide bounds, nor are necessarily guaranteed to con- verge without
Science Shorts: Observation versus Inference
ERIC Educational Resources Information Center
Leager, Craig R.
2008-01-01
When you observe something, how do you know for sure what you are seeing, feeling, smelling, or hearing? Asking students to think critically about their encounters with the natural world will help to strengthen their understanding and application of the science-process skills of observation and inference. In the following lesson, students make…
Inference networks for document retrieval
H. Turtle; W. B. Croft
1989-01-01
Abstract The use of inference networks,to support,document,retrieval is introduced.,A network-basead retrieval model,is described and compared,to conventional,probabilis- tic and Boolean models. 1,Introduction Network,representations,have,been,used,in information,retrieval since at least the early
Confidence distributions in statistical inference
NASA Astrophysics Data System (ADS)
Bityukov, Sergey; Krasnikov, Nikolai; Nadarajah, Saralees; Smirnova, Vera
2011-03-01
This paper reviews the new methodology for statistical inferences. Point estimators, confidence intervals and p—values are fundamental tools for frequentist statisticians. Confidence distributions, which can be viewed as "distribution estimators", are often convenient for constructing all of the above statistical procedures and more.
Temperature control with a neural fuzzy inference network
Chin-Teng Lin; Chia-Feng Juang; Chung-Ping Li
1996-01-01
We propose a neural fuzzy inference network (NFIN) suitable for adaptive temperature control of a water bath system. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. The performance of the NFIN is compared to that of
Inferring heuristic classification hierarchies from natural language input
NASA Technical Reports Server (NTRS)
Hull, Richard; Gomez, Fernando
1993-01-01
A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.
A parameter-adaptive dynamic programming approach for inferring cophylogenies
Daniel Merkle; Martin Middendorf; Nicolas Wieseke
2010-01-01
BACKGROUND: Coevolutionary systems like hosts and their parasites are commonly used model systems for evolutionary studies. Inferring the coevolutionary history based on given phylogenies of both groups is often done by employing a set of possible types of events that happened during coevolution. Costs are assigned to the different types of events and a reconstruction of the common history with
Bayesian-inference based recommendation in online social networks
Xiwang Yang; Yang Guo; Yong Liu
2011-01-01
In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct
No interpretation without representation: the role of domain-specic representations and inferences
Cosmides, Leda
that the different theories predict divergent outcomes, the results support the predictions of social contract theory-speci®c representation and inference systems, such as social contract algorithms and hazard management systems. Recently®c algorithms for representing and making inferences about (i) social contracts and (ii) reducing risk
Rogatch, Sarge
2012-01-01
It is a high-quality algorithm for hierarchical clustering of large software source code. This effectively allows to break the complexity of tens of millions lines of source code, so that a human software engineer can comprehend a software system at high level by means of looking at its architectural diagram that is reconstructed automatically from the source code of the software system. The architectural diagram shows a tree of subsystems having OOP classes in its leaves (in the other words, a nested software decomposition). The tool reconstructs the missing (inconsistent/incomplete/inexistent) architectural documentation for a software system from its source code. This facilitates software maintenance: change requests can be performed substantially faster. Simply speaking, this unique tool allows to lift the comprehensible grain of object-oriented software systems from OOP class-level to subsystem-level. It is estimated that a commercial tool, developed on the basis of this work, will reduce software mainte...
Baldwin, W.E.; Morton, R.A.; Putney, T.R.; Katuna, M.P.; Harris, M.S.; Gayes, P.T.; Driscoll, N.W.; Denny, J.F.; Schwab, W.C.
2006-01-01
Several generations of the ancestral Pee Dee River system have been mapped beneath the South Carolina Grand Strand coastline and adjacent Long Bay inner shelf. Deep boreholes onshore and high-resolution seismic-reflection data offshore allow for reconstruction of these paleochannels, which formed during glacial lowstands, when the Pee Dee River system incised subaerially exposed coastal-plain and continental-shelf strata. Paleochannel groups, representing different generations of the system, decrease in age to the southwest, where the modern Pee Dee River merges with several coastal-plain tributaries at Winyah Bay, the southern terminus of Long Bay. Positions of the successive generational groups record a regional, southwestward migration of the river system that may have initiated during the late Pliocene. The migration was primarily driven by barrier-island deposition, resulting from the interaction of fluvial and shoreline processes during eustatic highstands. Structurally driven, subsurface paleotopography associated with the Mid-Carolina Platform High has also indirectly assisted in forcing this migration. These results provide a better understanding of the evolution of the region and help explain the lack of mobile sediment on the Long Bay inner shelf. Migration of the river system caused a profound change in sediment supply during the late Pleistocene. The abundant fluvial source that once fed sand-rich barrier islands was cut off and replaced with a limited source, supplied by erosion and reworking of former coastal deposits exposed at the shore and on the inner shelf.
F-OWL: An Inference Engine for Semantic Web
NASA Technical Reports Server (NTRS)
Zou, Youyong; Finin, Tim; Chen, Harry
2004-01-01
Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.
Toddlers infer unobserved causes for spontaneous events
Muentener, Paul
Previous research suggests that children infer the presence of unobserved causes when objects appear to move spontaneously. Are such inferences limited to motion events or do children assume that unexplained physical events ...
Bayesian time series models and scalable inference
Johnson, Matthew James, Ph. D. Massachusetts Institute of Technology
2014-01-01
With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference ...
Helping Students to Make Inferences about Location.
ERIC Educational Resources Information Center
Tuttle, Harry Grover
1981-01-01
Discusses the importance of the study and practice of inference to help students develop independent reading ability. Presents classroom techniques which assist them in discerning inferences of location through transportation, landmarks, elements, actions, smells, and shapes. (Author/MES)
NASA Astrophysics Data System (ADS)
Komori, Shogo; Kagiyama, Tsuneomi; Fairley, Jerry P.
2014-08-01
The mass and heat budget of volcanic gases released from magma is critical to understanding a number of volcanic activities, including the ease with which magma can ascend. Due to its elevated temperature and salinity, the crustal electrical conductivity in a groundwater flow system increases through the addition of hydrothermal fluids which are produced by mixing of volcanic gases with meteoric-origin water. Therefore, the spatial extent of high electrical conductivity regions within groundwater flow systems may be used to evaluate the mass flux of volcanic gases to the systems. The present study attempts to estimate the mass flux of volcanic gases beneath the Unzen volcanic area in Southwest Japan, by developing a simple flow model of hydrothermal fluids and applying this model to the electrical conductivity structure of the area. The estimated mass flux of volcanic gases (104.8 ± 0.3 t/yr) yields results for CO2 flux (103.1 ± 0.3 t/yr) and magma input rate (100.1 ± 0.3 million m3/yr) that are consistent with those estimated by geochemical and geodetic observations. This suggests that volcanic gases are steadily released from magma into the overlying groundwater flow system beneath the area, and that effective degassing may be one of the factors controlling the relatively effusive style of recent volcanism at Unzen volcano.
[The mode of medical inference in the history of medicine].
Lee, T J
1994-01-01
In the primitive ages the system of thought about health and disease was a closed system of thought which had the premise of witchcraft. In the ancient and middle ages the problems of health and disease had been dealt with within logical thinking but the phenomena of human life had been explained metaphysically and the medical problems had been inferred from deductive logic. The abnormalities of health problems which were inferred from deductive logic had not been substantiated because anatomy, physiology and technology had not been advanced far enough. In the Renaissance and modern ages the knowledge of anatomy, physiology and pathology of living body have begun to increase. The human body could be explained in the terms of structure and function of the body as a machine. Approaching this way the disease has been understood as the abnormality of structure or function of the body and the problems of health and disease are inferred from inductive logic. ... PMID:11618919
The Laws of Natural Deduction in Inference by DNA Computer
Sosík, Petr
2014-01-01
We present a DNA-based implementation of reaction system with molecules encoding elements of the propositional logic, that is, propositions and formulas. The protocol can perform inference steps using, for example, modus ponens and modus tollens rules and de Morgan's laws. The set of the implemented operations allows for inference of formulas using the laws of natural deduction. The system can also detect whether a certain proposition a can be deduced from the basic facts and given rules. The whole protocol is fully autonomous; that is, after introducing the initial set of molecules, no human assistance is needed. Only one restriction enzyme is used throughout the inference process. Unlike some other similar implementations, our improved design allows representing simultaneously a fact a and its negation ~a, including special reactions to detect the inconsistency, that is, a simultaneous occurrence of a fact and its negation. An analysis of correctness, completeness, and complexity is included. PMID:25133261
FACIAL EXPRESSIONS OF EMOTION INTERPERSONAL TRAIT INFERENCES
Knutson, Brian
, as in the caseof trait infer- ence. The hypothesis that facial expressions of emotion (e.g., anger, disgust, fear, happiness, and sadness)affect subjects' interpersonal trait inferences (e.g., domi- nance and affiliation with either static or apparently mov- ing expressions. They inferred high dominance and affiliation from happy
Trace Inference, Curvature Consistency, and Curve Detection
Pierre Parent; Steven W. Zucker
1989-01-01
We describe a novel approach to curve inference based on curvature information. The inference procedure is divided into two stages: a trace inference stage, to which this paper is devoted, and a curve synthesis stage, which will be treated in a separate paper. It is shown that recovery of the trace of a curve requires estimating local models for the
Transitive and Pseudo-Transitive Inferences
ERIC Educational Resources Information Center
Goodwin, Geoffrey P.; Johnson-Laird, P. N.
2008-01-01
Given that A is longer than B, and that B is longer than C, even 5-year-old children can infer that A is longer than C. Theories of reasoning based on formal rules of inference invoke simple axioms ("meaning postulates") to capture such transitive inferences. An alternative theory proposes instead that reasoners construct mental models of the…
Inferring species trees from gene duplication episodes
J. Gordon Burleigh; Mukul S. Bansal; Oliver Eulenstein; Todd J. Vision
2010-01-01
Gene tree parsimony, which infers a species tree that implies the fewest gene duplications across a collection of gene trees, is a method for inferring phylogenetic trees from paralogous genes. However, it assumes that all duplications are independent, and therefore, it does not account for large-scale gene duplication events like whole genome duplications. We describe two methods to infer species
Bauer, Raymond T; Okuno, Junji; Thiel, Martin
2014-01-01
Sexual dimorphism in body size and weaponry was examined in two Cinetorhynchus shrimp species in order to formulate hypotheses on their sexual and mating systems. Collections of Cinetorhynchus sp. A and Cinetorhynchus sp. B were made in March, 2011 on Coconut Island, Hawaii, by hand dipnetting and minnow traps in coral rubble bottom in shallow water. Although there is overlap in male and female size, some males are much larger than females. The major (pereopod 1) chelipeds of males are significantly larger and longer than those of females. In these two Cinetorhynchus species, males and females have third maxillipeds of similar relative size, i.e., those of males are not hypertrophied and probably not used as spear-like weapons as in some other rhynchocinetid (Rhynchocinetes) species. Major chelae of males vary with size, changing from typical female-like chelae tipped with black corneous stout setae to subchelate or prehensile appendages in larger males. Puncture wounds or regenerating major chelipeds were observed in 26.1 % of males examined (N = 38 including both species). We interpret this evidence on sexual dimorphism as an indication of a temporary male mate guarding or "neighborhoods of dominance" mating system, in which larger dominant robustus males defend females and have greater mating success than smaller males. Fecundity of females increased with female size, as in most caridean species (500-800 in Cinetorhynchus sp. A; 300-3800 in Cinetorhynchus sp. B). Based on the sample examined, we conclude that these two species have a gonochoric sexual system (separate sexes) like most but not all other rhynchocinetid species in which the sexual system has been investigated. PMID:25561837
Bauer, Raymond T.; Okuno, Junji; Thiel, Martin
2014-01-01
Abstract Sexual dimorphism in body size and weaponry was examined in two Cinetorhynchus shrimp species in order to formulate hypotheses on their sexual and mating systems. Collections of Cinetorhynchus sp. A and Cinetorhynchus sp. B were made in March, 2011 on Coconut Island, Hawaii, by hand dipnetting and minnow traps in coral rubble bottom in shallow water. Although there is overlap in male and female size, some males are much larger than females. The major (pereopod 1) chelipeds of males are significantly larger and longer than those of females. In these two Cinetorhynchus species, males and females have third maxillipeds of similar relative size, i.e., those of males are not hypertrophied and probably not used as spear-like weapons as in some other rhynchocinetid (Rhynchocinetes) species. Major chelae of males vary with size, changing from typical female-like chelae tipped with black corneous stout setae to subchelate or prehensile appendages in larger males. Puncture wounds or regenerating major chelipeds were observed in 26.1 % of males examined (N = 38 including both species). We interpret this evidence on sexual dimorphism as an indication of a temporary male mate guarding or “neighborhoods of dominance” mating system, in which larger dominant robustus males defend females and have greater mating success than smaller males. Fecundity of females increased with female size, as in most caridean species (500–800 in Cinetorhynchus sp. A; 300–3800 in Cinetorhynchus sp. B). Based on the sample examined, we conclude that these two species have a gonochoric sexual system (separate sexes) like most but not all other rhynchocinetid species in which the sexual system has been investigated. PMID:25561837
Kumagai, H.; Chouet, B.A.; Nakano, M.
2002-01-01
We present a detailed description of temporal variations in the complex frequencies of long-period (LP) events observed at Kusatsu-Shirane Volcano. Using the Sompi method, we analyze 35 LP events that occurred during the period from August 1992 through January 1993. The observed temporal variations in the complex frequencies can be divided into three periods. During the first period the dominant frequency rapidly decreases from 5 to 1 Hz, and Q of the dominant spectral peak remains roughly constant with an average value near 100. During the second period the dominant frequency gradually increases up to 3 Hz, and Q gradually decreases from 160 to 30. During the third period the dominant frequency increases more rapidly from 3 to 5 Hz, and Q shows an abrupt increase at the beginning of this period and then remains roughly constant with an average value near 100. Such temporal variations can be consistently explained by the dynamic response of a hydrothermal crack to a magmatic heat pulse. During the first period, crack growth occurs in response to the overall pressure increase in the hydrothermal system caused by the heat pulse. Once crack formation is complete, heat gradually changes the fluid in the crack from a wet misty gas to a dry gas during the second period. As heating of the hydrothermal system gradually subsides, the overall pressure in this system starts to decrease, causing the collapse of the crack during the third period.
NASA Technical Reports Server (NTRS)
Reichle, Rolf H.; De Lannoy, Gabrielle J. M.
2012-01-01
The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.
Barrio, María; Negrier, Philippe; Tamarit, Josep Ll; Pardo, Luis C; Mondieig, Denise
2007-08-01
The phases diagrams of the two-component systems CCl4 +CBr2Cl2 and CBrCl3 + CBr2Cl2 have been determined by means of X-ray powder diffraction and thermal analysis techniques from the low-temperature ordered phase to the liquid state. The isomorphism relationship between the stable orientationally disordered (OD) face-centered cubic (FCC) phases of CBrCl3 and CBr2Cl2 and the metastable OD FCC phase (monotropic behavior with respect to the OD rhombohedral stable phase) of CCl4 has been put into evidence throughout the continuous evolution of the lattice parameters and the existence of the two-phase equilibrium [FCC + L] for the whole range of composition in both two-component systems. This equilibrium interferes, for the CCl4 +CBr2Cl2 system, with a rhombohedral (R) plus liquid ([R + L]) equilibrium giving rise to a peritectic invariant. In addition, whatever the system, [R + FCC] equilibrium also interferes with the low-temperature equilibria between the low-temperature monoclinic (C2/c) phase and the OD R and FCC phases. In regards to the low-temperature monoclinic phases, isomorphism is evidenced, and by means of Rietveld profile refinement, any ordering of the molecules by varying the fractional occupancy of the halogen sites has been detected. The thermodynamic assessment, conducted by means of the concept of crossed isopolymorphism, coherently reproduces all the involved equilibria and provides a coherent set of data for the thermodynamic properties of nonexperimentally available phase transitions of pure compound CBr2Cl2 which enables us to obtain the topological properties of its pressure-temperature phase diagram and to infer the existence of a high-pressure R phase for such a compound. PMID:17602520
Efficient Inference of Partial Types
Dexter Kozen; Jens Palsberg; Michael I. Schwartzbach
1992-01-01
Partial types for the ?-calculus were introduced by Thatte in 1988 (3) as a means of typing objects that are not typable with simple types,such as heterogeneous lists and persistent data. In that paper he showed that type inference for partial types was semidecidable. Decidability remained open until quite recently,when O'Keefe and Wand (2) gave an exponential time algorithm for
Differential geometry of statistical inference
Shun-ichi Amari; O. E. Barndorff-Nielsen; R. E. Kass; S. L. Lauritzen; C. R. Rao
1987-01-01
A higher-order asymptotic theory of statistical inference is presented in a unified manner in the differential-geometrical\\u000a framework. The first-, second- and third-order efficiencies of estimators are obtained in terms of the curvatures and connections\\u000a of submanifolds related to both the model and estimator. The first-, second and third- order powers of a two-sided (unbiased)\\u000a test is also obtained in terms
Inference Networks for Document Retrieval
Howard R. Turtle; W. Bruce Croft
1990-01-01
The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilis- tic and Boolean models. Network representations have been used in information retrieval since at least the early 1960's. Networks have been used to support diverse retrieval functions, including browsing (TC89), document clustering (CroSO), spreading activation search (CK87), support
Procrustean Statistical Inference of Deformations
NASA Astrophysics Data System (ADS)
Hossainali, M.; Becker, M.; Groten, E.
2011-01-01
A two step method has been devised for the statistical inference of deformation changes. In the first step of this method and based on Procrustes analysis of deformation tensors, the significance of the change in a time or space series of deformation tensors is statistically analyzed. In the second step significant change(s) in deformations are localized. In other words, they are assigned to certain parameters of deformation tensor. This is done using the Global Model Test. Because of the key role of Procrustes analysis in the proposed method for the inference of deformation changes, it has been given the name of Procrustean Statistical Inference of Deformations. The method has been implemented to synthetic and real deformations. The 3D-deformation tensors of a regional GPS network in the Kenai Peninsula, for analyzing the spatial variation of deformation tensors or the change of deformation within the study area, and a local GPS network in France, for analyzing the temporal variation of deformation tensors or the change of deformation in time at every point of the network in the study area have been used for illustrating the practical application of the proposed method.
NASA Astrophysics Data System (ADS)
Chiodini, G.; Caliro, S.; Cardellini, C.; De Martino, P.; Petrillo, Z.
2012-12-01
The temporal variation of magmatic fluid release at Campi Flegrei caldera is investigated using numerical simulations of the hydrothermal system constrained by diffuse CO2 emission data and by the chemical composition of fumarolic vents. The main aim is to understand the recent dynamics of Campi Flegrei, where hundreds of thousands of people live in an area subjected since the middle of the 20th century to a long term crisis characterized by several episodes of ground uplift and correspondent seismic swarms (bradyseism), the most significant of which occurred in A.D. 1950-1953, 1970-1972, and 1982-1984 (maximum total ground uplift ~4 m). In 1998, the first measurements of diffuse degassing from the Solfatara crater, the most active zone of Campi Flegrei, revealed the very intense release of hydrothermal- magmatic CO2 (~1500 t/d) and of thermal energy (~100 W) highlighting that the expulsion of deep fluids is the main form of energy loss from the entire caldera and suggesting an important role of magma degassing during the crisis. The hydrothermal system of Solfatara recently underwent large changes, including compositional variations of fumarolic effluents, compositional homogenization of the fluid released at different vents, changes in the pattern of diffuse degassing, increases in the pressures of the system, and increases in the temperature and in the flow rate of the fumaroles. Furthermore, after 20 yr of subsidence, an uplift period started in 2005. Comparing long-term series of geochemical signals with ground deformation and seismicity, we show that these changes are caused by repeated injections of magmatic fluid into the hydrothermal system. The frequency of the degassing episodes has increased in the last years, causing the almost continuous increase of the magmatic component of the fumaroles, pulsed uplift episodes and swarms of low magnitude earthquakes. Physical simulations of the process show that total injected fluid masses in each episode of magma degassing are the same order of magnitude as those emitted during small to medium size volcanic eruptions, and their cumulative curve highlights a current period of increasing activity.
Pathway network inference from gene expression data
2014-01-01
Background The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. PMID:25032889
NASA Astrophysics Data System (ADS)
Gounelle, Matthieu; Chaussidon, Marc; Rollion-Bard, Claire
2013-02-01
A search for short-lived 10Be in 21 calcium-aluminum-rich inclusions (CAIs) from Isheyevo, a rare CB/CH chondrite, showed that only 5 CAIs had 10B/11B ratios higher than chondritic correlating with the elemental ratio 9Be/11B, suggestive of in situ decay of this key short-lived radionuclide. The initial (10Be/9Be)0 ratios vary between ~10-3 and ~10-2 for CAI 411. The initial ratio of CAI 411 is one order of magnitude higher than the highest ratio found in CV3 CAIs, suggesting that the more likely origin of CAI 411 10Be is early solar system irradiation. The low (26Al/27Al)0 [<= 8.9 × 10-7] with which CAI 411 formed indicates that it was exposed to gradual flares with a proton fluence of a few 1019 protons cm-2, during the earliest phases of the solar system, possibly the infrared class 0. The irradiation conditions for other CAIs are less well constrained, with calculated fluences ranging between a few 1019 and 1020 protons cm-2. The variable and extreme value of the initial 10Be/9Be ratios in carbonaceous chondrite CAIs is the reflection of the variable and extreme magnetic activity in young stars observed in the X-ray domain.