An Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling
Aickelin, Uwe
combines the fuzzy logic qualitative approach and adaptive neural network capabil- ities towards betterAn Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Survival;An Exploration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Survival Hazlina
A Tabu-Search Based Neuro-Fuzzy Inference System for Fault Diagnosis
Rizvi, Syed Z.
A Tabu-Search Based Neuro-Fuzzy Inference System for Fault Diagnosis Haris M. Khalid S.Z. Rizvi@kfupm.edu.sa). Abstract: This paper presents a novel hybrid Tabu Search (TS) Subtractive Clustering (SC) based Neuro. Keywords: Tabu Search, Subtractive Clustering, Neuro-Fuzzy, Soft Computing, Artificial Neural Network
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.
Na, Man Gyun [Chosun University (Korea, Republic of); Oh, Seungrohk [Dankook University (Korea, Republic of)
2002-11-15
A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.
NASA Astrophysics Data System (ADS)
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
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
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
Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela
Hurtado, Nuri; Torres, Julio
2014-01-01
The high accuracy on inferrring of rocks properties, such as permeability ($k$), is a very useful study in the analysis of wells. This has led to development and use of empirical equations like Tixier, Timur, among others. In order to improve the inference of permeability we used a hybrid Neuro-Fuzzy System (NFS). The NFS allowed us to infer permeability of well, from data of porosity ($\\phi$) and water saturation ($Sw$). The work was performed with data from wells VCL-1021 (P21) and VCL-950 (P50), Block III, Maracaibo Lake, Venezuela. We evaluated the NFS equations ($k_{P50,i}(\\phi_i,Sw_i)$) with neighboring well data ($P21$), in order to verify the validity of the equations in the area. We have used ANFIS in MatLab.
Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.
Ubeyli, Elif Derya
2009-10-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261
H. J. B. Saldaña; C. S. Ca?rdenas
2010-01-01
This paper presents a digital system architecture for a two-input one-output zero order ANFIS (Adaptive Neuro-Fuzzy Inference System) and its implementation on an FPGA (Field Programmable Gate Array) using VHDL (VHSIC Hardware Description Language). The designed system is used for nonlinear function generation. First, a nonlinear function is chosen and off-line training is carried out using MATLAB ANFIS to obtain
Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.
Akgundogdu, Abdurrahim; Kurt, Serkan; Kilic, Niyazi; Ucan, Osman N; Akalin, Nilgun
2010-12-01
Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN). PMID:20703607
Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy
Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy Technique Approach Intelligent Center Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy Technique Approach Final Project neuro-fuzzy techniques is used for non-linear system identification, output estimation, and fault
NASA Astrophysics Data System (ADS)
Moghaddamnia, A.; Ghafari Gousheh, M.; Piri, J.; Amin, S.; Han, D.
2009-01-01
Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. It has been found that ANN and ANFIS techniques have much better performances than the empirical formulas (for the test data set, ANN R2 = 0.97, ANFIS R2 = 0.92 and Marciano R2 = 0.54). Between ANN and ANFIS, ANN model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process is quite complicated. In this research, the Gamma test (GT) has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration. More studies are needed to gain wider experience about this data selection tool and how it could be used in assessing the validation data.
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.
Ubeyli, Elif Derya
2009-03-01
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286
NASA Astrophysics Data System (ADS)
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)
Hayati, M.; Rashidi, A. M.; Rezaei, A.
2011-01-01
This paper presents application of adaptive neuro-fuzzy inference system (ANFIS) for prediction of the grain size of nanocrystalline nickel coatings as a function of current density, saccharin concentration and bath temperature. For developing ANFIS model, the current density, saccharin concentration and bath temperature are taken as input, and the resulting grain size of the nanocrystalline coating as the output of the model. In order to provide a consistent set of experimental data, the nanocrystalline nickel coatings have been deposited from Watts-type bath using direct current electroplating within a large range of process parameters i.e., current density, saccharin concentration and bath temperature. Variation of the grain size because of the electroplating parameters has been modeled using ANFIS, and the experimental results and theoretical approaches have been compared to each other as well. Also, we have compared the proposed ANFIS model with artificial neural network (ANN) approach. The results have shown that the ANFIS model is more accurate and reliable compared to the ANN approach.
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.
Combining Logical-Type Neuro Fuzzy Systems
Marcin Korytkowski; Robert Nowicki; Leszek Rutkowski; Rafal Scherer
2006-01-01
\\u000a Boosting is one of the most popular methods of multiple classification. In the paper we propose a method for merging several\\u000a logical-type neuro-fuzzy systems that come from boosting ensemble into one neuro-fuzzy system. Thanks to this we can use all\\u000a rule-bases as one system.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson
2013-01-01
The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs. PMID:24109851
NASA Astrophysics Data System (ADS)
Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan
2010-05-01
Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes. As a final conclusion, the results obtained from the study showed that the ANFIS modeling is a very useful and powerful tool for the regional landslide susceptibility assessments. However, the results to be obtained from the ANFIS modeling should be assessed carefully because the overlearning may cause misleading results. To prevent overlerning, the numbers of membership functions of inputs and the number of training epochs should be selected optimally and carefully.
NASA Astrophysics Data System (ADS)
Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.
The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.
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…
Klaus Dalinghaus Realisierung und Optimierung eines Neuro-Fuzzy Systems
Kallenrode, May-Britt
Klaus Dalinghaus Realisierung und Optimierung eines Neuro-Fuzzy Systems zur Erkennung rhythmischer: Thorsten Hinrichs © Institute of Cognitive Science #12;Realisierung und Optimierung eines Neuro Realisierung und Optimierung eines Neuro-Fuzzy Systems zur Erkennung rhythmischer Muster MAGISTERARBEIT ZUR
Neuro-fuzzy modeling for dynamic system identification
Jyh-Shing Roger Jang
1996-01-01
This paper presents the continued work of a previously proposed ANFIS (Adaptive Neuro-Fuzzy Fuzzy Inference System) architecture with emphasis on the applications to dynamic system identification. We demonstrate the use of ANFIS for the hair dryer modeling problem and compare its performance with the ARX model
Mehdi Jalali-Heravi; Anahita Kyani
2008-01-01
The purpose of this study was to develop the structure–toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811
NASA Astrophysics Data System (ADS)
Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad
2012-05-01
Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.
Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
NASA Astrophysics Data System (ADS)
Teimouri, Reza; Sohrabpoor, Hamed
2013-12-01
Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.
Efe, Mehmet Önder
@ieee.org Abstract: Adaptive neuro-fuzzy inference systems exhibit both the numeric power of neural networks, which is a suitable combination of neural networks and fuzzy inference systems, can exhibit the above in an Adaptive Neuro Fuzzy Inference System M. Onder Efe Bogazici University, Electrical and Electronic
An Ensemble of Logical-type Neuro-fuzzy Systems
RafaÅ Scherer
2011-01-01
Neuro-fuzzy classifiers are characterized by incorporation of the expert knowledge into their construction. The most popular neuro-fuzzy systems are Mamdani-type systems. The main groups of neuro-fuzzy systems are also Takagi–Sugeno and logical-type systems. The latter were very rarely studied in the literature, however it was shown that logical-type reasoning transpired to be better suited for classification tasks whereas Mamdani-type reasoning
Torshabi, Ahmad Esmaili
2014-12-01
In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works. PMID:25412886
NASA Astrophysics Data System (ADS)
Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin
2013-07-01
The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.
Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail
2013-01-01
Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department. PMID:24052927
NASA Astrophysics Data System (ADS)
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2015-06-01
This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.
NASA Astrophysics Data System (ADS)
Heidary, Saeed; Setayeshi, Saeed
2015-01-01
This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous 99mTc/201Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of 201Tl (77±10% keV) and 99mTc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.
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
Civelekoglu, G; Yigit, N O; Diamadopoulos, E; Kitis, M
2009-01-01
This work evaluated artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modelling methods to estimate organic carbon removal using the correlation among the past information of influent and effluent parameters in a full-scale aerobic biological wastewater treatment plant. Model development focused on providing an adaptive, useful, practical and alternative methodology for modelling of organic carbon removal. For both models, measured and predicted effluent COD concentrations were strongly correlated with determination coefficients over 0.96. The errors associated with the prediction of effluent COD by the ANFIS modelling appeared to be within the error range of analytical measurements. The results overall indicated that the ANFIS modelling approach may be suitable to describe the relationship between wastewater quality parameters and may have application potential for performance prediction and control of aerobic biological processes in wastewater treatment plants. PMID:19759450
Skin Cancer Recognition by Using a Neuro-Fuzzy System
Salah, Bareqa; Alshraideh, Mohammad; Beidas, Rasha; Hayajneh, Ferial
2011-01-01
Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%. PMID:21340020
NASA Astrophysics Data System (ADS)
Ajay Kumar, M.; Srikanth, N. V.
2014-03-01
In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.
Aalborg Universitet Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy
Andreasen, Søren Juhl
Aalborg Universitet Methanol Reformer System Modeling and Control using an Adaptive Neuro., & Sahlin, S. L. (2012). Methanol Reformer System Modeling and Control using an Adaptive Neuro Neuro-Fuzzy Inference System approach Kristian K. Justesen, John Andersen, Mikkel P. Ehmsen, Søren J
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems L.M. Reyneri, M@iet.unipi.it e.mail: chiaberge@polito.it Abstract 1 This paper describes CINTIA, a Neuro-Fuzzy real embedded systems. The pro- posed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems L.M. Reyneri , M@iet.unipi.it e.mail: chiaberge@polito.it Abstract 1 This paper describes CINTIA, a Neuro-Fuzzy real embedded systems. The pro- posed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems
Reyneri, Leonardo
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems B. Bona, S intelligent con- trol paradigms mixing Neuro-Fuzzy algorithms with nite state automata and or digital con of control problems. Neuro-Fuzzy systems, specially when com- bined with DSPs can solve e ciently both
Biogeography-Based Optimization of Neuro-Fuzzy System Parameters for Diagnosis of Cardiac Disease
Simon, Dan
Biogeography-Based Optimization of Neuro-Fuzzy System Parameters for Diagnosis of Cardiac Disease through P wave features. In particular, we design a neuro-fuzzy network trained with a new evolutionary algorithm called biogeography-based optimization (BBO). The neuro-fuzzy network recognizes and classifies P
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
Sanyal, Sugata
or efficiency. In this paper, two machine-learning paradigms, Artificial Neural Networks and Fuzzy Inference of misuse and anomaly based detection techniques in combination with neural networks to make it adaptiveAdaptive Neuro-Fuzzy Intrusion Detection Systems Sampada Chavan, Khusbu Shah, Neha Dave
Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin
2014-06-01
A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. PMID:24638918
NASA Astrophysics Data System (ADS)
Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika
2014-08-01
The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.
Yalcin, Hasan; Ozturk, Ismet; Karaman, Safa; Kisi, Ozgur; Sagdic, Osman; Kayacier, Ahmed
2011-05-01
In this study, natural compounds including gallic acid, ellagic acid, quercetin, ?-carotene, and retinol were used as antioxidant agents in order to prevent and decrease oxidation in hazelnut oil. Quercetin showed the strongest antioxidative effect among the antioxidative agents, during storage. The accuracy of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models was studied to estimate the oil samples' peroxide value (PV), free fatty acid (FFA), and iodine values (IV). The root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R(2)) statistics were used to evaluate the models' accuracy. Comparison of the models showed that the ANFIS model performed better than the ANN and multiple linear regressions (MLR) models for estimating the PV, FFA, and IV. The values of R(2) and RMSE were found to be 0.9966 and 2.51, 0.6269 and 88.55, 0.5120 and 101.8 for the ANFIS, ANN, and MLR models for PV in testing period, respectively. The MLR was found to be insufficient for estimating various properties of the oil samples. PMID:22417373
NASA Astrophysics Data System (ADS)
Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo
2013-03-01
The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.
NASA Astrophysics Data System (ADS)
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.
Jalali-Heravi, Mehdi; Kyani, Anahita
2008-06-01
The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol-water partition coefficient (logP), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results (q(2)=0.828 and RMSE=0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters. PMID:18499226
NASA Astrophysics Data System (ADS)
He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun
2014-02-01
Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.
NASA Astrophysics Data System (ADS)
Iphar, Melih; Yavuz, Mahmut; Ak, Hakan
2008-11-01
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.
NASA Astrophysics Data System (ADS)
Fallah Ghalhary, G. A.; Khoshhal, J.; Habibi Nokhandan, M.
2009-09-01
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 Jan and March 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, Outgoing Long Wave Radiation (OLR), zonal wind and meridional wind. 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
Neuro-fuzzy systems for intelligent robot navigation and control under uncertainty
Wei Li
1995-01-01
This paper describes neuro-fuzzy systems for intelligent robot navigation and control under uncertainty. First, we present a new neuro-fuzzy system architecture for behavior navigation of a mobile robot in unknown environments. In this neuro-fuzzy system, a neural network is used to process range information for understanding distribution of obstacles in local regions; while fuzzy sets and a rule base are
Neuro-fuzzy control of vertical vibrations in railcars using magnetorheological dampers
Atray, Vipul Sunil
2002-01-01
-fuzzy controller. A pair of magnetorheological (MR) dampers is designed and installed in a rail truck. Two neuro-fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neuro Fuzzy Controller (NEFCON), are used to emulate behavior of the MR dampers...
System identification of smart structures using a wavelet neuro-fuzzy model
NASA Astrophysics Data System (ADS)
Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar
2012-11-01
This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.
NASA Astrophysics Data System (ADS)
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.
Hongping Liu; V. Chandrasekar
2000-01-01
Fuzzy logic and Neuro-Fuzzy systems for the classification of hydrometeor type based on polarimetric radar measurements is developed. The hydrometeor classification system is implemented where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system. Five radar measurements,
Neuro-fuzzy system for medical image processing
S. Asif Hussain; M. Praveen Raju
2010-01-01
Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We
Diagnosis of dermatological diseases by a neuro-fuzzy system
Giovanna Castellano; Ciro Castiello; Anna Maria Fanelli; C. Leone
2003-01-01
In this paper we present the applica- tion of a particular neuro-fuzzy sys- tem, named KERNEL, to the problem of differential diagnosis of erythemato- squamous diseases, which represents a major problem in dermatology. A mul- tistep learning strategy is adopted to obtain, starting directly from available data, a fuzzy rule base that can be used to identify the particular disease.
A Neuro-Fuzzy Systems for Control Applications F. Berardi, M. Chiaberge, E. Miranda and L.M. Reyneri
Reyneri, Leonardo
and computationally inten- sive task. Neural networks and fuzzy systems neuro-fuzzy systems, in general are raising - ITALY e.mail marcello@polimage.polito.it, fax: ++39 11 564 4099 Keywords Arti cial Neural Networks examples, the adaptation capability, etc. Furthermore the availability of dedicated neuro-fuzzy processors
Reyneri, Leonardo
A Walking Hexapod Controlled by a Neuro-Fuzzy System F. Berardi, M. Chiaberge, E. Miranda and L, a Neuro-Fuzzy system for control applications. The sys- tem is based on a custom neural device that can intelligent control al- gorithms mixing neuro-fuzzy algorithms with nite state automata and is used to control
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
Merrikh-Bayat, Farnood
2011-01-01
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault-tolerant, all synaptic weights in our proposed method are always non-negative and there is no need to precisely adjust them. Finally, this structure is hierarchically expandable and can compute operations in real time since it is implemented through analog circuits. Simulation results show the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain.
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
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.
Newer applications of neuro-fuzzy systems for risk assessment and diagnostics in medicine
Andor Sagi; Anita Sabo; Tibor Szakáll
2010-01-01
The application of neuro-fuzzy systems in the fields of medical sciences are increasing in parallel with the rapid evolution of diagnostic and treatment procedures in medicine. Contemporary literature provides us with many examples that this cooperation has brought many benefits in the fields of diagnostics and risk assessment in medicine. In Serbia, similar to those more developed countries, these systems
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
Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback
Schurter, Kyle Christopher
2000-01-01
Parameter specification of a fuzzy inference system (HS) with the aid of artificial neural networks allows the creation of complex, multi-dimensional models that are computationally efficient and numerically robust. An adaptive neuro-fuzzy inference...
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.
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.
Simulation of elastic tissues in virtual medicine using neuro-fuzzy systems
NASA Astrophysics Data System (ADS)
Radetzky, Arne; Nuernberger, Andreas; Pretschner, Dietrich P.
1998-06-01
To improve the benefit of surgical simulators for education and research a visual convincing modeling of the operation scenario and the involved tissues is not sufficient. It is also necessary to simulate the deformation and resulting inner forces of tissue under influence of external forces caused by, for example, medical instruments or gravity. In this paper, we present a hybrid neuro-fuzzy system, which was designed for the description and simulation of tissues. The neuro-fuzzy system can be used to simulate the physical behavior like stiffness, viscosity and inertia of deformable or elastic tissues in surgical simulation. The parameters of a physical model or prior expert knowledge in the form of linguistic terms can be used to initialize the network parameters. Using a neural network structure, local changes to the system like cuts or ruptures can be performed during simulation. As an application example, some simulation results in the area of gynecological laparoscopy are given.
Development of an adaptive neuro-fuzzy method for supply air pressure control in HVAC system
Wu Jian; Cai Wenjian
2000-01-01
An adaptive neuro-fuzzy (ANF) method is developed for the supply air pressure control loop of a heating, ventilation and air-conditioning (HVAC) system. Although a well-tuned PID controller performs well around normal working points, its tolerance to process parameter variations is severely affected due to the nature of PID controllers. The ANF controller developed overcomes this weakness. The controller design involves
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.
Naive Bayes Classifiers Using Neuro-Fuzzy Learning1
Borgelt, Christian
Improving Naive Bayes Classifiers Using Neuro-Fuzzy Learning1 A. NÂ¨urnberger, C. Borgelt, and A classification performance. Another promi- nent type of classifiers are neuro-fuzzy classifica- tion systems there are certain structural similarities between a neuro-fuzzy classifier and a naive Bayes classifier, the idea
A neuro-fuzzy system for extracting environment features based on ultrasonic sensors.
Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José
2009-01-01
In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160
A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors
Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José
2009-01-01
In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160
Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system
NASA Astrophysics Data System (ADS)
Pourmahyabadi, M.; Mohammad Nejad, S.
2009-10-01
The most common methods applied in the analysis of photonic crystal fibers (PCFs) are finite difference time/frequency domain (FDTD/FDFD) method and finite element method (FEM). These methods are very general and reliable (well tested). They describe arbitrary structure but are numerically intensive and require detailed treatment of boundaries and complex definition of calculation mesh. So these conventional models that simulate the photonic response of PCFs are computationally expensive and time consuming. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time. In this article, an artificial intelligence method such as Neuro-Fuzzy system is used to establish a model that can predict the properties of PCFs. Simulation results show that this model is remarkably effective in predicting the properties of PCF such as dispersion, dispersion slope and loss over the C communication band.
Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions. PMID:26001214
Position control of ionic polymer metal composite actuator based on neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Nguyen, Truong-Thinh; Yang, Young-Soo; Oh, Il-Kwon
2009-07-01
This paper describes the application of Neuro-Fuzzy techniques for controlling an IPMC cantilever configuration under water to improve tracking ability for an IPMC actuator. The controller was designed using an Adaptive Neuro-Fuzzy Controller (ANFC). The measured input data based including the tip-displacements and electrical signals have been recorded for generating the training in the ANFC. These data were used for training the ANFC to adjust the membership functions in the fuzzy control algorithm. The comparison between actual and reference values obtained from the ANFC gave satisfactory results, which showed that Adaptive Neuro-Fuzzy algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFC performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.
NASA Astrophysics Data System (ADS)
Bazzazi, Abbas Aghajani; Esmaeili, Mohammad
2012-12-01
Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (
A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system
Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken
2010-12-15
This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)
NASA Astrophysics Data System (ADS)
Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok
2015-01-01
This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
NASA Technical Reports Server (NTRS)
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
Liu, Feng; Quek, Chai; Ng, Geok See
2007-06-01
There are two important issues in neuro-fuzzy modeling: (1) interpretability--the ability to describe the behavior of the system in an interpretable way--and (2) accuracy--the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models. PMID:17444763
Neuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1
Neuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1 , Lijun Jiang2 of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Ex- perimental-then rules acquired by training a neuro-fuzzy system can solve similar weight selection problems. 1
Chou, Chien-Hsing (Ister)
to demonstrate its performance. Key-words: reinforcement learning, neural network, neuro-fuzzy system 1 the learning abilities of neural networks in the design of fuzzy systems has recently become a very active research area, e.g., fuzzy adaptive learning control network [1], back-propagation fuzzy systems [2
A Neuro-Fuzzy Approach to Hybrid Intelligent B. Lazzerini, L.M. Reyneri, M. Chiaberge
Reyneri, Leonardo
A Neuro-Fuzzy Approach to Hybrid Intelligent Control B. Lazzerini, L.M. Reyneri, M. Chiaberge Abstract This paper presents a neuro-fuzzy approach to the devel- opment of high-performance real with feed- back in hybrid neuro-fuzzy systems. Section V describes a few hardware implementations for hybrid
Neuro-Fuzzy Hardware: Design, Development and Performance L.M. Reyneri
Reyneri, Leonardo
Neuro-Fuzzy Hardware: Design, Development and Performance L.M. Reyneri Dipartimento di Elettronica. This paper introduces hardware implementations of neuro-fuzzy systems, describes the technologies commonly implementation tech- niques. A. Neuro-Fuzzy Uni cation Until a few years ago, NNs, FSs and WNs were consid- ered
Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration: a comparative study
Shaun H. Lee; Robert J. Howlett; Cyril Crua; Simon D. Walters
2007-01-01
The aim of this study was to demonstrate the effectiveness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of diesel spray penetration length in the cylinder of a diesel internal combustion engine. The technique involved extraction of necessary representative features from a collection of raw image data. A comparative evaluation of two fuzzy-derived techniques for modelling fuel spray
A. Halvai Niasar; H. Moghbeli; R. Kazemi
2003-01-01
This paper, investigates the traction control of an electric vehicle (EV) that is equipped with two motor drives. A new yaw moment control scheme via an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The ANFIS is an attractive compromise between the adaptability of a neural network and interpretability of a fuzzy inference system. In a 2WD EV, because of independent
NASA Astrophysics Data System (ADS)
?encan ?ahin, Arzu; Yaz?c?, Hilmi
2012-07-01
In this study, energy and exergy analysis of the Afyon geothermal district heating system (AGDHS) in Afyon, Turkey using artificial neural network (ANN) and adaptive neuro-fuzzy (ANFIS) methods is carried out. Actual system data in the analysis of the AGDHS are used. The results of ANN are compared with ANFIS in which the same data sets are used. ANN model is slightly better than ANFIS in determining the energy and exergy rates. In addition, new formulations obtained from ANN are presented for the determination of the energy and exergy rates of the AGDHS. The R2-values obtained when unknown data were used in the networks were 0.999999847 and 0.99999997 for the energy and exergy rates respectively, which are very satisfactory.
Approximation abilities of neuro-fuzzy networks
NASA Astrophysics Data System (ADS)
Mrówczy?ska, Maria
2010-01-01
The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.
Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration
Shaun H. Lee; Robert J. Howlett; Simon D. Walters; Cyril Crua
2005-01-01
This paper describes a comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration in the cylinders of a diesel internal combustion engine. The first model is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second model used an adaptive neuro-fuzzy inference system (ANFIS), where automatic
Development of an intelligent neuro-fuzzy maneuver identification system for autonomous aircraft
NASA Astrophysics Data System (ADS)
Krishnamurthy, Karthik
2000-10-01
This dissertation reports an investigation of the design of intelligent systems for the high-level control of autonomous aircraft. In a departure from recent work in this field, an attempt has been made to synthesize a high-level control architecture that emulates a human pilot's reasoning capabilities. The system architecture uses pilot-type classifications of aircraft modes (the various maneuvers that pilots are trained to execute) within all decision-making and reasoning processes. A flight control system structured in terms of these modes offers scope for efficient combination of concepts from artificial intelligence, control theory and aviation practice. A critical component of this intelligent flight controller is an automated mode inference system. This innovative system extracts high-level knowledge of the current maneuver (or segment of the overall mission) from sensed measurements of dynamic state variables. Using a blend of soft computing approaches, this inference engine consistently identifies the correct maneuver being flown, even in the presence of moderate sensor noise and data ambiguities. In the process of creating this inference engine, a novel scheme to generate training data sets for neural networks has been developed. This data generation scheme permits complete coverage of the aircraft's capability envelope; this coverage is achieved without recourse to the voluminous flight data (actual or simulated) normally required to train neural networks. The data generation scheme thus significantly reduces developmental effort. Apart from this innovation, pilot-like techniques to cope with the phenomenon of chatter (where identification rapidly switches back-and-forth between modes) have been developed and implemented within the inference system. This dissertation also discusses the development of logic to interpret and implement commands from remote operators, using high-level knowledge of the current mission segment. This knowledge is used to contextually understand such commands, and to decide on a future course of action in terms of a sequence of maneuvers. A family of optimal controllers to track such maneuvers has also been formulated. The formulation permits pilot-like combinations of regulation and tracking functions. A few such maneuver-based controllers are presented, and used to investigate the development of logic to monitor the accuracy of mathematical models of the aircraft.
Abnormal red blood cells detection using adaptive neuro-fuzzy system.
Babazadeh Khameneh, Nahid; Arabalibeik, Hossein; Salehian, Piruz; Setayeshi, Saeed
2012-01-01
Features like size, shape, and volume of red blood cells are important factors in diagnosing related blood disorders such as iron deficiency and anemia. This paper proposes a method to detect abnormality in red blood cells using cell microscopic images. Adaptive local thresholding and bounding box methods are used to extract inner and outer diameters of red cells. An adaptive network-based fuzzy inference system (ANFIS) is used to classify blood samples to normal and abnormal. Accuracy of the proposed method and area under ROC curve are 96.6% and 0.9950 respectively. PMID:22356952
A novel neuro-fuzzy controller to enhance the performance of vehicle semi-active suspension systems
Mohammad Biglarbegian; William Melek; Farid Golnaraghi
2008-01-01
This paper proposes a neuro-fuzzy (NF) strategy to implement semi-active suspension in passenger vehicles. The proposed method is composed of two parts: a NF controller (NFC), to establish an efficient controller strategy to improve ride comfort and road handling (RCH), and an inverse mapping to estimate the semi-active suspension current. To effectively estimate the current needed to control the semi-active
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.
PREOPERATIVE OVARIAN CANCER DIAGNOSIS USING NEURO-FUZZY APPROACH E.O. Madu, V. Stalbovskaya, B the Adaptive Network based Fuzzy Inference System (ANFIS). Our model predicts ovarian cancer malignancy using operating characteristic curve of 0.85. Keywords: ovarian cancer, medical diagnosis, neural networks, neuro
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
A Neuro-Fuzzy Approach as Medical Diagnostic R. Brause, F. Friedrich
Brause, R.
A Neuro-Fuzzy Approach as Medical Diagnostic Interface R. Brause, F. Friedrich J life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro
Applications of Fuzzy and Neuro-Fuzzy in Biomedical Health Sciences
R. Ranjan; A. Awasthi; N. Aggarawal; J. Gulati
2006-01-01
Fuzzy and neuro fuzzy logic concepts have been used for variety of applications, where machine intelligence can be used for the system. The fuzzy logic concept has been used for a number of biomedical applications also. We have used fuzzy and neuro fuzzy approach for tackling surgery procedures of a patient suffering from stress urinary incontinence (SUI). We have further
3D City Model Generator: The Application of Neuro-Fuzzy Systems in CAD
neural networks, fuzzy systems, image processing, pattern recognition, and machine learning constitute patterns of complex relations between inputs and outputs as neural networks and represent them as a set of fuzzy rules in linguistic forms. Using the fuzzy MLP, the system is capable of adapting
NASA Astrophysics Data System (ADS)
Prakash, S.; Sinha, S. K.
2015-09-01
In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.
Use of an adaptive neuro-fuzzy system to characterize root distribution patterns
Technology Transfer Automated Retrieval System (TEKTRAN)
Root-soil relationships are pivotal to understanding crop growth and function in a changing environmental. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statist...
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.
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.
De Luca, Carlo J.
were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system, adaptive neuro-fuzzy inference system, artificial neural network, elec- tromyography, stroke, wearableIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 17, NO. 6, DECEMBER 2009
Adaptive neuro-fuzzy control of a flexible manipulator
Lianfang Tian; Curtis Collins
2005-01-01
This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system,
Efe, Mehmet Önder
International Journal of Systems Science, 2001, volume 32, number 4, pages 513± 521 Derivation of a parameter stabilizing training criterion for adaptive neuro-fuzzy inference systems in motion control M for adaptive neuro-fuzzy inference systems. The algorithm combines the error back-propagation algorithm
A Neuro-Fuzzy Method of Power Disturbances Recognition and Reduction Leon Reznik1
Reznik, Leon
neuro-fuzzy applications in power engineering: stabilizing power systems at a generation stage. INTRODUCTION Modern power generation and delivery systems are required to provide significant improvement of power quality, monitoring variant disturbances occurred in generation, transmission and distribution
Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis
2010-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks. PMID:20411596
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.
Estimating the crowding level with a neuro-fuzzy classifier
NASA Astrophysics Data System (ADS)
Boninsegna, Massimo; Coianiz, Tarcisio; Trentin, Edmondo
1997-07-01
This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.
Daily soil temperature modeling using neuro-fuzzy approach
NASA Astrophysics Data System (ADS)
Hosseinzadeh Talaee, P.
2014-11-01
Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models' performances at arid and semiarid locations showed that the CANFIS models' performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.
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.
Recognition of Handwritten Arabic words using a neuro-fuzzy network
NASA Astrophysics Data System (ADS)
Boukharouba, Abdelhak; Bennia, Abdelhak
2008-06-01
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
Surinder K. Jassar; T. Behan; L. Zhao; Z. Liao
2009-01-01
Inferential sensors are used to infer the critical control variables that are otherwise difficult, if not impossible, to measure in broad range of engineering fields. All inferential sensors are based on an inferential modelling module that represents the dynamics between the inputs and the outputs. Two commonly used artificial intelligence based approaches for the development of the inferential modelling modules
Neuro-Fuzzy Phasing of Segmented Mirrors
NASA Technical Reports Server (NTRS)
Olivier, Philip D.
1999-01-01
A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.
Data center selection based on neuro-fuzzy inference systems in cloud computing environments
Joon-Min Gil; Jong Hyuk Park; Young-Sik Jeong
A high-quality service for applications in cloud computing environments is guaranteed by making efficient use of resources\\u000a in data centers. Applications should be allocated to resources suitable for the load of data centers to achieve this. The\\u000a complex and dynamic nature of the load prevents the proper selection of one of multiple data centers and fails to meet the\\u000a demands
Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting
Abdalla Kablan
2009-01-01
The prediction of financial time series is a very complicated process. An initial look at financial time series gives the impression that they are random in nature. If true, this would make the forecast, and therefore the trading, of such series exceptionally difficult. The efficient market hypothesis states that the current price contains all available information in the market. This
NASA Astrophysics Data System (ADS)
Setia, Ronald; May, Gary S.
2006-02-01
Excimer laser ablation is used for microvia formation in the microelectronics packaging industry. With continuing advancement of laser systems, there is an increasing need to offset capital equipment investment and lower equipment downtime. This paper presents a neuro-fuzzy methodology for in-line failure detection and diagnosis of the excimer laser ablation process. Response data originating directly from laser tool sensors and the characterization of microvias were used as failure symptoms for potential deviations in four laser system parameters from their corresponding baseline values. The response characteristics consist of via diameter, via wall angle, and via resistance. Resistance measurements on copper deposited in the ablated vias were performed to characterize the degree to which debris remaining inside the vias affected quality. The laser system parameters include laser fluence, shot frequency, number of pulses, and helium pressure flow. The adaptive neuro-fuzzy inference system (ANFIS) was trained and subsequently validated for its capability in evidential reasoning using the data collected. Results indicated only a single false alarm occurred in 19 possible failure detection scenarios. In failure diagnosis, a single false alarm and a single missed alarm occurred.
WAVELET PACKET TRANSFORM AND NEURO-FUZZY APPROACH TO HANDWRITTEN CHARACTER RECOGNITION
Schwiebert, Loren
with real world problems, a fuzzy logic system is used for classification purpose. A neural network system and classified using IF ... THEN rules, and given to a neural network recognition system. This method is moreWAVELET PACKET TRANSFORM AND NEURO-FUZZY APPROACH TO HANDWRITTEN CHARACTER RECOGNITION SREELA SASI
SALVATORE PENNACCHIO; FRANCESCO MARIA RAIMONDI
This paper introduces a new model of control system for mobile robot navigation in unknown environments. This model follows two approach, the first one is based on fuzzy logic and the second one is based on neuro fuzzy. Both approaches are directed to generate a collision free path avoiding static obstacles. This paper presents a possible optimization skill for the
Adaptive Output Tracking of Transverse Flux Machines Using Neuro-Fuzzy Approach
A. Babazadeh; H. R. Karimi
2006-01-01
This paper deals with adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing Takagi-Sugeno type neuro-fuzzy networks. The technique of feedback linearization and H control are used to design an adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance on the
Neuro-fuzzy knowledge processing in intelligent learning environments
Magoulas, George D.
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro used to drive pedagogical decisions depending on the student learning style. The neuro
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
A neuro-fuzzy based approach for output tracking of transverse flux machines
A. Babazadeh; H. R. Karimi; B. Moshiri
2005-01-01
This paper describes a design for adaptive control of transverse flux permanent magnet machines as nonlinear systems with unknown nonlinearities by utilizing Takagi-Sugeno-Kang type neuro-fuzzy networks. The technique of feedback linearization and Hinfin control are used to design the adaptive control law for compensating the unknown nonlinear parts, such the effect of cogging torque, as a disturbance on the rotor
Study on Weld Quality Control of Resistance Spot Welding Using a Neuro-Fuzzy Algorithm
Yansong Zhang; Guanlong Chen; Zhongqin Lin
\\u000a Resistance spot welding (RSW) is widely utilized as a joining technique for automobile industry. However, good weld quality\\u000a evaluation method has not yet been developed in plant environment. It is necessary to achieve real-time inspection of RSW.\\u000a This paper proposed a neuro-fuzzy algorithm to predict weld quality online. An experimental system was developed to measure\\u000a electrode displacement curve. Accordingly based
EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features.
Hsu, Wei-Yen
2010-06-15
In this paper, a feature extraction method through the time-series prediction based on the adaptive neuro-fuzzy inference system (ANFIS) is proposed for brain-computer interface (BCI) applications. The ANFIS time-series prediction together with multiresolution fractal feature vectors (MFFVs) is applied for feature extraction in motor imagery (MI) classification. The features are extracted from the electroencephalography (EEG) signals recorded from subjects performing left and right MI. Two ANFISs are trained to perform time-series predictions for respective left and right MI data. Features obtained from the difference of MFFVs between the predicted and actual signals are then calculated through a window of EEG signals. Finally, a simple linear classifier, namely linear discriminant analysis (LDA), is used for classification. The proposed method is estimated with classification accuracy and the area under the receiver operating characteristics curve (AUC) on six subjects from two data sets. I also assess the performance of proposed method by comparing it with well-known linear adaptive autoregressive (AAR) model, AAR time-series prediction, and neural network (NN) time-series prediction. The results indicate that ANFIS time-series prediction together with MFFV features is a promising method in MI classification. PMID:20381529
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
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris DePaul University in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
Iterative Identification of Neuro-Fuzzy-Based Hammerstein Model with Global Convergence
Ge, Shuzhi Sam
Iterative Identification of Neuro-Fuzzy-Based Hammerstein Model with Global Convergence Li Jia, Min Engineering, National University of Singapore, Singapore 119260 In this paper, a neuro-fuzzy-based model of the neuro-fuzzy-based Hammerstein model, and an updating algorithm guaranteeing the global convergence
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris De in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers
Reyneri, Leonardo
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers M. Chiaberge , G. Di Bene are commonly integrated into the neuro-fuzzy ap- proach, which has proven well adapted to non- linear control- tectures by appropriately combining and opti- mizing them. However, although the neuro-fuzzy approach alone
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...
Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling
Marwala, Tshilidzi; Chakraverty, Snehashish
2007-01-01
This paper presents a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.
Neuro-fuzzy rule generation: survey in soft computing framework.
Mitra, S; Hayashi, Y
2000-01-01
The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided. PMID:18249802
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.
NASA Astrophysics Data System (ADS)
Lin, J.; Zheng, Y. B.
2012-07-01
The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.
Wang, Yu; Winters, Jack M
2005-01-01
Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs). Methods The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. Results The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. Conclusion A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective. PMID:15985181
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Havangi, Ramazan; Teshnehlab, Mohammad
2010-01-01
Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as...
Reyneri, Leonardo
Abstract Arti cial Neural Networks ANNs and Fuzzy Systems FS are high parallel structures that consist. Miranda and L.M. Reyneri LIM - Interdepartmental Laboratory of Mechatronics Politecnico di Torino C on Neural Net- work and Fuzzy logic techniques while the DSP handles several other tasks: complementary
Chakraborty, Debrup
110 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 1, JANUARY 2004 A Neuro-Fuzzy Scheme it in a separate phase, offline prior to the main clas- sification task. This paper proposes a neuro-fuzzy scheme quite well. Index Terms--Classification, feature analysis, neuro-fuzzy sys- tems, rule extraction. I
CardeÃ±osa, JesÃºs
Data Mining in Incomplete Numerical and Categorical Data Sets: a Neuro-Fuzzy Approach P. Rey incomplete numerical and categorical data sets using an extension of an existing neuro-fuzzy approach evolutionary algorithms and neural networks. Neuro-fuzzy computation is one of the most popular hybridizations
Simon, Dan
CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO be overcome using a hybrid neuro-fuzzy prediction model that is better capable of uncovering complex, non-linear interactions between ECG parameters. We created a neuro- fuzzy network that was able to classify the patients
On-Line Heart Beat Recognition Using Hermite Polynomials And Neuro-Fuzzy
Osowski, Stanislaw
On-Line Heart Beat Recognition Using Hermite Polynomials And Neuro-Fuzzy Network Tran Hoai Linh, Stanis¨aw Osowski and Maciej Stodolski Abstract The paper presents the neuro-fuzzy approach-mail: sto@iem.pw.edu.pl). #12;ON-LINE HEART BEAT RECOGNITION USING HERMITE POLYNOMIALS AND NEURO
Fuzzy Sets and Systems 132 (2002) 189199 www.elsevier.com/locate/fss
Simon, Dan
2002-01-01
descent and with adaptive neuro-fuzzy inference system (ANFIS) based optimization of fuzzy membership that have been used include neural networks [5,10], evolu- tionary programming [12], cell mapping (aFuzzy Sets and Systems 132 (2002) 189199 www.elsevier.com/locate/fss Training fuzzy systems
Proceedings of the 2012 Industrial and Systems Engineering Research Conference X. Wei and A. Kusiak
Kusiak, Andrew
neural network is applied to construct the optimization model. Due to high computational complexity reported in the literature. Data-mining algorithms such as neural networks have shown success in building models of the biogas production process. An adaptive neuro-fuzzy inference system was applied
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.
NASA Astrophysics Data System (ADS)
Costanzo, Vincenzo; da Silva, Ana; Hurtado, Nuri
2010-05-01
We have used the hybrid algorithm of neuro fuzzy logic (NFL), to establish a correlation between global climatic changes (benthic foraminiferal d18O data), experimental S-ratios and magnetic susceptibility (?), in 44 samples of the Colombian stratigraphic well Saltarín 1A (Llanos foreland basin). ? and S-ratios were linked to global d18O data based on a constant accumulation rate for the stratigraphic interval flanked by the two age constrains available. A good inference (over 64%) is obtained using 4 fuzzy clusters or TKS type relationships. A stronger correlation is perhaps prevented by the likely influence of local and regional tectonic events and climatic changes that could have affected the Colombian Llanos foreland basin during Miocene times. For the Guayabo and León lithologies, it seems that the late diagenesis of the primary magnetic minerals and the assumption of a constant accumulation rate might have a minor influence on these results.
Neuro-fuzzy based model of batch fermentation of Kluyveromyces marxianus var. lactis MC5
Ilkova, Tatiana; Petrov, Mitko
2014-01-01
In this work a neuro-fuzzy based model of a whey batch fermentation process by a strain Kluyveromyces marxianus var. lactis MC5 is presented. A three-layered neuro-fuzzy network is realized. The simulation results are compared with conventional models (based on mass balance and differential equations). The neuro-fuzzy model provides a better fitness and allows inclusion of linguistic variables (such as colour, smell, taste, morphophysiology, etc.). The accuracy is approximately equal to this achieved by a conventional neural network. The proposed approach is flexible (with regard to the process model) and quite robust (with regard to the possible uncertainties and to the optimization surface). Future work will focus on applying this approach for modelling of different biotechnological processes. PMID:26019585
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.
Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.
Carrascal, A; Manrique, D; Ríos, J; Rossi, C
2003-01-01
This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach. PMID:14629866
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; 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
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.
Neuro-fuzzy models as an IVIVR tool and their applicability in generic drug development.
Opara, Jerneja; Legen, Igor
2014-03-01
The usefulness of neuro-fuzzy (NF) models as an alternative in vitro-in vivo relationship (IVIVR) tool and as a support to quality by design (QbD) in generic drug development is presented. For drugs with complicated pharmacokinetics, immediate release drugs or nasal sprays, suggested level A correlations are not capable to satisfactorily describe the IVIVR. NF systems were recognized as a reasonable method in comparison to the published approaches for development of IVIVR. Consequently, NF models were built to predict 144 pharmacokinetic (PK) parameter ratios required for demonstration of bioequivalence (BE) for 88 pivotal BE studies. Input parameters of models included dissolution data and their combinations in different media, presence of food, formulation strength, technology type, particle size, and spray pattern for nasal sprays. Ratios of PK parameters Cmax or AUC were used as output variables. The prediction performance of models resulted in the following values: 79% of models have acceptable external prediction error (PE) below 10%, 13% of models have inconclusive PE between 10 and 20%, and remaining 8% of models show inadequate PE above 20%. Average internal predictability (LE) is 0.3%, and average external predictability of all models results in 7.7%. In average, models have acceptable internal and external predictabilities with PE lower than 10% and are therefore useful for IVIVR needs during formulation development, as a support to QbD and for the prediction of BE study outcome. PMID:24477942
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.
Simon, Dan
Classification of Atrial Fibrillation prone Patients using Electrocardiographic Parameters in Neuro of statistics-based attempts to predict AF occurrence may be overcome using a hybrid neuro-fuzzy prediction evocated by the Writing Group in [1]. The following 15 crisp valued parameters were considered for neuro
NASA Astrophysics Data System (ADS)
Shiri, Jalal; Nazemi, Amir Hossein; Sadraddini, Ali Ashraf; Landeras, Gorka; Kisi, Ozgur; Fard, Ahmad Fakheri; Marti, Pau
2013-02-01
SummaryAccurate estimation of reference evapotranspiration is important for irrigation scheduling, water resources management and planning and other agricultural water management issues. In the present paper, the capabilities of generalized neuro-fuzzy models were evaluated for estimating reference evapotranspiration using two separate sets of weather data from humid and non-humid regions of Spain and Iran. In this way, the data from some weather stations in the Basque Country and Valencia region (Spain) were used for training the neuro-fuzzy models [in humid and non-humid regions, respectively] and subsequently, the data from these regions were pooled to evaluate the generalization capability of a general neuro-fuzzy model in humid and non-humid regions. The developed models were tested in stations of Iran, located in humid and non-humid regions. The obtained results showed the capabilities of generalized neuro-fuzzy model in estimating reference evapotranspiration in different climatic zones. Global GNF models calibrated using both non-humid and humid data were found to successfully estimate ET0 in both non-humid and humid regions of Iran (the lowest MAE values are about 0.23 mm for non-humid Iranian regions and 0.12 mm for humid regions). non-humid GNF models calibrated using non-humid data performed much better than the humid GNF models calibrated using humid data in non-humid region while the humid GNF model gave better estimates in humid region.
Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds
Gini, Giuseppina
1 Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds Dan Neagu1 in the developed connectionist structures, improve over individual models for toxicity prediction. 1 INTRODUCTION this especially challenging. In toxicity prediction there are many variables: the toxicological endpoint
Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines
algorithms to enable direct drive from permanent magnet (PM) synchronous motors[3]. #12;Among AC drives, the permanent magnet (PM) motors have became popular, due to its high torque to current ration, large powerNeuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines Kasim M. Al
Combining classifiers of pesticides toxicity through a neuro-fuzzy approach
Gini, Giuseppina
Combining classifiers of pesticides toxicity through a neuro-fuzzy approach Emilio Benfenati1 an improvement in combining the results of five classifiers applied in toxicity of pesticides. Nevertheless in toxicity of pesticides characterization, and knowledge discovery. 1 Introduction Quantitative structure
A neuro-fuzzy based parameter identification of an indirect vector-controlled induction motor drive
L. R. Valdenebro; J. R. Hernandez; E. Bim
1999-01-01
In this paper an adaptive field oriented control of induction motor drive is proposed. The adaptive scheme uses a neuro-fuzzy approach for the identification of the rotor time constant, which is used to adjust the estimate of the slip angular speed. First, a fuzzy logic estimator was developed and tuned, then the fuzzy estimator was implemented by a dynamic backpropagation
Tahir Ayata; Ertu?rul Çam; Osman Y?ld?z
2007-01-01
Natural ventilation in living and working places provides both circulation of clear air and a decrease of indoor temperature, especially during hot summer days. In addition to openings, the dimension ratio and position of buildings play a significant role to obtain a uniform indoor air velocity distribution. In this study, the potential use of natural ventilation as a passive cooling
Likhitruangsilp, Visit
2002-01-01
This research illustrates semi-active structural control of a three-story nonlinear building using magnetorheological dampers and a neuro-fuzzy algorithm. The structure being studied was developed for a third generation benchmark problem...
Adaptive-Neuro-Fuzzy-Based Sensorless Control of a Smart-Material Actuator
Ali Sadighi; Won-jong Kim
2011-01-01
In this paper, adaptive-neuro-fuzzy-based sensorless control of a smart-material actuator is presented. The smart ma- terial that we used to develop a novel type of linear actuator is Terfenol-D. The peristaltic motion in the actuator is generated by inducing a traveling magnetic field inside the Terfenol-D element. The sensorless control of the actuator is based on an observation illustrating a
Adaptive neuro-fuzzy control of ionic polymer metal composite actuators
NASA Astrophysics Data System (ADS)
Thinh, Nguyen Truong; Yang, Young-Soo; Oh, Il-Kwon
2009-06-01
An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation.
NASA Astrophysics Data System (ADS)
Baraldi, Andrea; Binaghi, Elisabetta; Blonda, Palma N.; Brivio, Pietro A.; Rampini, Anna
1998-10-01
Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.
Mitchell, Richard
On Relation Between Neuro-Fuzzy and CMAC Controller - 1 IEEE SMC UK&RI Applied Cybernetics © Dr Richard Mitchell 2005 On the Relation between Neuro Fuzzy and CMAC Controller Dr N.H.Siddique, School Engineering, University of Reading This paper proposes a learning mechanism where the rule base of the neuro
Application of neuro-fuzzy methods to gamma spectroscopy
NASA Astrophysics Data System (ADS)
Grelle, Austin L.
Nuclear non-proliferation activities are an essential part of national security activities both domestic and abroad. The safety of the public in densely populated environments such as urban areas or large events can be compromised if devices using special nuclear materials are present. Therefore, the prompt and accurate detection of these materials is an important topic of research, in which the identification of normal conditions is also of importance. With gamma-ray spectroscopy, these conditions are identified as the radiation background, which though being affected by a multitude of factors is ever present. Therefore, in nuclear non-proliferation activities the accurate identification of background is important. With this in mind, a method has been developed to utilize aggregate background data to predict the background of a location through the use of an Artificial Neural Network (ANN). After being trained on background data, the ANN is presented with nearby relevant gamma-ray spectroscopy data---as identified by a Fuzzy Inference System - to create a predicted background spectra to compare to a measured spectra. If a significant deviation exists between the predicted and measured data, the method alerts the user such that a more thorough investigation can take place. Research herein focused on data from an urban setting in which the number of false positives was observed to be 28 out of a total of 987, representing 2.94% error. The method therefore currently shows a high rate of false positives given the current configuration, however there are promising steps that can be taken to further minimize this error. With this in mind, the method stands as a potentially significant tool in urban nuclear nonproliferation activities.
Methodology for assessing system performance loss within a proactive maintenance framework
Cocheteux, Pierre; Levrat, Eric; Iung, Benoît
2009-01-01
Maintenance plays now a critical role in manufacturing for achieving important cost savings and competitive advantage while preserving product conditions. It suggests moving from conventional maintenance practices to predictive strategy. Indeed the maintenance action has to be done at the right time based on the system performance and component Remaining Useful Life (RUL) assessed by a prognostic process. In that way, this paper proposes a methodology in order to evaluate the performance loss of the system according to the degradation of component and the deviations of system input flows. This methodology is supported by the neuro-fuzzy tool ANFIS (Adaptive Neuro-Fuzzy Inference Systems) that allows to integrate knowledge from two different sources: expertise and real data. The feasibility and added value of such methodology is then highlighted through an application case extracted from the TELMA platform used for education and research.
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.
Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling
NASA Astrophysics Data System (ADS)
Samadzadegan, Farhad; Azizi, Ali; Hahn, Michael; Lucas, Curo
Three-dimensional object recognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper a novel approach for object recognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany.
Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed
NASA Technical Reports Server (NTRS)
Olivier, Philip D.
2002-01-01
NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.
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
Reactive navigation for autonomous guided vehicle using neuro-fuzzy techniques
NASA Astrophysics Data System (ADS)
Cao, Jin; Liao, Xiaoqun; Hall, Ernest L.
1999-08-01
A Neuro-fuzzy control method for navigation of an Autonomous Guided Vehicle robot is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by as terrain and a set of distinct objects, such as obstacles and landmarks. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Neural network and fuzzy logic control techniques can improve real-time control performance for mobile robot due to its high robustness and error-tolerance ability. For a mobile robot to navigate automatically and rapidly, an important factor is to identify and classify mobile robots' currently perceptual environment. In this paper, a new approach of the current perceptual environment feature identification and classification, which are based on the analysis of the classifying neural network and the Neuro- fuzzy algorithm, is presented. The significance of this work lies in the development of a new method for mobile robot navigation.
Hany M. Hasanien; S. M. Muyeen; Junji Tamura
2010-01-01
This paper presents a novel adaptive neuro-fuzzy controller applies on transverse flux linear motor for controlling its speed. The proposed controller presents fuzzy logic controller with self tuning scaling factors based on artificial neural network structure. It has two input variables and one control output variable. Firstly the fuzzy logic control rules are described then NN architecture is represented to
Pal, Sankar Kumar
feature subsets using the aforesaid fuzzy evaluation index without neural networks. Results demonstrating.) in information, in an ecient manner. Arti®cial Neural Networks (ANNs), having the capability of fault toleranceUnsupervised feature selection using a neuro-fuzzy approach Jayanta Basak, Rajat K. De, Sankar K
Pal, Sankar Kumar
366 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 2, MARCH 2000 Unsupervised Feature. Index Terms--Feature selection/extraction, fuzzy feature evalu- ation index, layered networks, soft Evaluation: A Neuro-Fuzzy Approach Sankar K. Pal, Fellow, IEEE, Rajat K. De, Member, IEEE, and Jayanta Basak
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.
Verifying Stability of Dynamic Soft-Computing Systems
NASA Technical Reports Server (NTRS)
Wen, Wu; Napolitano, Marcello; Callahan, John
1997-01-01
Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.
NASA Astrophysics Data System (ADS)
Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok
2015-05-01
This work presents a novel neuro-fuzzy controller (NFC) for car-driver's seat-suspension system featuring magnetorheological (MR) dampers. The NFC is built based on the algorithm for building adaptive neuro-fuzzy inference systems (ANFISs) named B-ANFIS, which has been developed in Part 1, and fuzzy logic inference systems (FISs). In order to create the NFC, the following steps are performed. Firstly, a control strategy based on a ride-comfort-oriented tendency (RCOT) is established. Subsequently, optimal FISs are built based on a genetic algorithm (GA) to estimate the desired damping force that satisfies the RCOT corresponding to the road status at each time. The B-ANFIS is then used to build ANFISs for inverse dynamic models of the suspension system (I-ANFIS). Based on the FISs, the desired force values are calculated according to the status of road at each time. The corresponding exciting current value to be applied to the MR damper is then determined by the I-ANFIS. In order to validate the effectiveness of the developed neuro-fuzzy controller, control performances of the seat-suspension systems featuring MR dampers are evaluated under different road conditions. In addition, a comparative work between conventional skyhook controller and the proposed NFC is undertaken in order to demonstrate superior control performances of the proposed methodology.
Protein contact map prediction using multi-stage hybrid intelligence inference systems.
Abu-Doleh, Anas A; Al-Jarrah, Omar M; Alkhateeb, Asem
2012-02-01
Proteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein's 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets. PMID:22079474
Characterizing root distribution with adaptive neuro-fuzzy analysis
Technology Transfer Automated Retrieval System (TEKTRAN)
Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics...
A neuro-fuzzy architecture for real-time applications
NASA Technical Reports Server (NTRS)
Ramamoorthy, P. A.; Huang, Song
1992-01-01
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.
Classification of Biomedical Images Using Neuro Fuzzy Approach
Shashikala Tapaswi; Ramesh Chandra Joshi
2004-01-01
\\u000a The prime requirement for medical imaging systems is to be able to display images relating to a particular disease, there\\u000a is increasing interest in the use of Image Retrieval techniques to aid diagnosis by identifying similar past cases. One area\\u000a where computers have scored great success in biomedicine has been medical imaging. In biomedicine, searching digital bio-medical\\u000a images is a
Speed Control of Multi Level Inverter Designed DC Series Motor with Neuro-Fuzzy Controllers
MadhusudhanaRao, G
2009-01-01
This paper describes the speed control of a DC series motor for an accurate and high-speed performance. A neural network based controlling operation with fuzzy modeling is suggested in this paper. The driver units of these machines are designed with a Multi-level inverter operation and are controlled by a common current control mechanism for an accurate and efficient driving technique for DC series motor. The neuro-fuzzy logic control technique is introduced to eliminate uncertainties in the plant parameters of the DC Series motors, and also considered as potential candidate for different applications to prove adequacy of the proposed control algorithm through simulations. The simulation result with such an approach is made and observed efficient over other controlling technique.
Neural Network-based Fuzzy Modeling of the Student in Intelligent Tutoring Systems
Magoulas, George D.
Neural Network-based Fuzzy Modeling of the Student in Intelligent Tutoring Systems R that makes use of neuro-fuzzy synergism to evaluate the students in the context of an intelligent tutoring to evaluate information regarding student's knowledge and cognitive abilities in a domain area. The neuro-fuzzy
NASA Astrophysics Data System (ADS)
Abbassi, B.; Hafizi, M. K.; Ashtari, A.
2009-04-01
Surface Nuclear Magnetic Resonance (SNMR) is a new geophysical method currently developed for shallow investigations of aquifers. Compared to the other geophysical methods, SNMR is a water selective method. Therefore, hydraulic properties of media are achievable through SNMR investigations. Relative to classical electrical methods, SNMR still costs much. In order to reduce the expense of the survey, it should be better to perform a sufficient electrical tomography in the region, and then few SNMR sounding for acquiring aquifer properties. Then it is possible to use the Neuro-fuzzy estimator for estimating permeability in the regions without SNMR data. Neuro-fuzzy estimator is tested on a 3D synthetic model. A case study is also presented to explain the applicability of the Method. The study area is located in Iran. The scope of the survey is determining the best well site for drilling. The transmisitivity map of the aquifer is contoured for selecting the best permeable site.
Neuro-fuzzy chip to handle complex tasks with analog performance.
de Jesus Navas-Gonzalez, R; Vidal-Verdu, F; Rodriguez-Vazquez, A
2003-01-01
This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input-output delay, and precision, performs as a fully analog implementation. However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core. Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture are smaller than those of its purely analog counterparts simply because most rules are implemented through programming. The paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype. This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 /spl mu/m standard technology. It has two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided. PMID:18244584
NASA Astrophysics Data System (ADS)
Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.
2011-08-01
Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.
NASA Astrophysics Data System (ADS)
Rafael Garcia-Lazaro, Jose; Arbelo, Manuel; Moreno-Ruiz, Jose A.; Piedra-Fernandez, Ja
Remote sensing data from different instruments (AVHRR, MODIS, LANDSAT) and spatial resolutions (30m, 500m, 1Km, 4Km) were used to assess the impact of the spatial resolution in burned area mapping. Uncertainty was estimated with a neuro-fuzzy classifier. High resolution remote sensing images (Landsat5/TM) and ground data were used initially to select diverse scenes affected by the fire. The study region was located in the north-west region of the Iberian Peninsula, where several fires occurred in August 2006. A pixel approach neuro-fuzzy classifier was designed to identify burned areas on those high resolution scenes but only using those bands in similar spectral region, comparable between sensors. The classifier was applied to all of the images in order to compute the burned area uncertainty driven by the image resolution. Results show the inverse relationship between the spatial resolution of the images and the burned areas in terms of uncertainty. Burned pixel neighbourhood conditions could be used by the classifier in order to improve uncertainty burned area estimations.
Geun Hyeong Lee; Seul Jung
2008-01-01
This paper presents a mechatronics system for intelligent control education. The inverted pendulum system is designed and built to fit an educational kit as an intelligent mechatronics system. The neuro-fuzzy control method whose structure is the Tagaki-Sugeno model is introduced to control the pendulum. The Takagi-Sugeno(T-S) neuro-fuzzy control structure is implemented on the DSP board built in our Lab. Parameters
Ana Da Silva; Vincenzo Costanzo-Álvarez; Nuri Hurtado; Milagrosa Aldana; Germán Bayona; Oswaldo Guzmán; Diego López-Rodríguez
2010-01-01
In this work we have assessed the hybrid algorithm of NeuroFuzzy logic (NFL), to establish a correlation between global climatic\\u000a changes (benthic foraminiferal ?18O data), experimental S-ratio (factor characterizing stability of remanent magnetization) and magnetic susceptibility (?).\\u000a Magnetic proxies have been measured in 44 samples of the Colombian stratigraphic well Saltarín 1A (distal Llanos foreland\\u000a basin). ? and Sratios were
Darvishi, Sam; Al-Ani, Ahmed
2007-01-01
The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of Brain-Computer Interface (BCI). We propose here to use an Adaptive Neuron-Fuzzy Inference System (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. The continuous wavelet transform will be used to extract highly representative features from selected scales. The performance of ANFIS will be compared with the well-known support vector machine classifier. PMID:18002681
Estimation of dew point temperature using neuro-fuzzy and neural network techniques
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal
2013-11-01
This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.
Clustering of noisy image data using an adaptive neuro-fuzzy system
NASA Technical Reports Server (NTRS)
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images
Vassilis S. Kodogiannis; M. Boulougoura; John N. Lygouras; Ilias Petrounias
2007-01-01
Wireless capsule endoscopy (WCE) constitutes a recent technology in which a capsule with micro-camera attached to it, is swallowed by the patient. This paper presents an integrated methodology for detecting abnormal patterns in WCE images. Two issues are being addressed, including the extraction of texture features from the texture spectra in the chromatic and achromatic domains from each colour component
Neuro-fuzzy model of superelastic shape memory alloys with application to seismic engineering
Ozbulut, Osman Eser
2009-05-15
Shape memory alloys (SMAs) have recently attracted much attention as a smart material that can be used in passive protection systems such as energy dissipating devices and base isolation systems. For the purpose of ...
Prediction of low back pain with two expert systems.
Sari, Murat; Gulbandilar, Eyyup; Cimbiz, Ali
2012-06-01
Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation. PMID:20978929
System Support for Forensic Inference
NASA Astrophysics Data System (ADS)
Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan
Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.
Annals of Operations Research ISSN 0254-5330
Kusiak, Andrew
· Wastewater treatment facility · Data-mining algorithms · Neural networks · Adaptive neuro-fuzzy model 1 metrics. Based on these metrics, a model built by the Adaptive Neuro-Fuzzy Inference System algorithm has studies to build comprehensive models have been recommended (Pontes and Pinto 2006). Fuzzy and neural
Inferring Regulatory Systems with Noisy Pathway Information
Zell, Andreas
Inferring Regulatory Systems with Noisy Pathway Information C. Spieth, F. Streichert, N. Speer of inferring gene regulatory networks becomes more and more feasible. The major problem of most of these pathways is that they are very often faulty or describe only parts of a regulatory system due
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.
Neurocomputing 69 (2006) 586614 A new approach to fuzzy classifier systems and its
Chou, Chien-Hsing (Ister)
2006-01-01
-based fuzzy adaptive learning network [36], generic self-organizing fuzzy neural network [53], fuzzy; Neural networks; Neuro-fuzzy systems; Classifier systems; Bucket brigade algorithm ARTICLE IN PRESS www fuzzy systems. Thus, incorporating the learning abilities of neural networks into the design of fuzzy
Using intelligent system approach for very short-term load forecasting purposes
L. C. M. de Andrade; I. N. da Silva
2010-01-01
The main purpose of this paper is to achieve a comparative analysis among Autoregressive Integrated Moving Average model, Artificial Neural Networks and Adaptive Neuro-Fuzzy System techniques for load demand forecasting in distribution substations. The system inputs are three load demand time series, which are composed by data measured at intervals of five minutes each, during seven days, from substations located
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.
Parsopoulos, Konstantinos
Maps (FCMs) constitute a modeling methodology that combines fuzzy logic and neural networks (Kosko to the class of neuro fuzzy systems, which are able to incorporate human knowledge and adapt it through Media, Inc. Manufactured in The Netherlands. Fuzzy Cognitive Maps Learning Using Particle Swarm
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.
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.
An Introduction to Fuzzy State Automata L.M. Reyneri
Reyneri, Leonardo
. They are derived from the integration of traditional nite state automata and neuro-fuzzy systems, where a nite of the neuro-fuzzy system accordingly. The main di erence with respect to existing systems is that the states controllers which can be trained by examples", or both of them neuro-fuzzy integration. Neuro-fuzzy systems
FPGA Implementation of Fuzzy Inference System for Embedded Applications
- based FIS has been used to control the operation of a permanent magnet (PM) motor in a washing machine of the whole system. A fuzzy inference system has been implemented on an FPGA, and used to control a PM motor:- FPGA, Fuzzy logic, Fuzzy inference system, PM motor, Washing machine. INTRODUCTION The real world
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.
Rule-based inference model for the Kansei Engineering System
Sun-mo Yang; Mitsuo Nagamachi; Soon-yo Lee
1999-01-01
Kansei Engineering has been applied to product development for customer satisfaction based on ergonomic technology. The system is composed of three parts such as Kansei analysis, inference mechanism, and presentation technologies. The inference mechanism by which human Kansei is translated into design elements plays an important role in Kansei Engineering. The reasoning logic in the system must satisfy several conditions.
Inference by replication in densely connected systems.
Neirotti, Juan P; Saad, David
2007-10-01
An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance. PMID:17995074
Inference by replication in densely connected systems
Neirotti, Juan P.; Saad, David [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…
NASA Astrophysics Data System (ADS)
Pasam, Gopi Krishna; Manohar, T. Gowri
2015-07-01
Determination of available transfer capability (ATC) requires the use of experience, intuition and exact judgment in order to meet several significant aspects in the deregulated environment. Based on these points, this paper proposes two heuristic approaches to compute ATC. The first proposed heuristic algorithm integrates the five methods known as continuation repeated power flow, repeated optimal power flow, radial basis function neural network, back propagation neural network and adaptive neuro fuzzy inference system to obtain ATC. The second proposed heuristic model is used to obtain multiple ATC values. Out of these, a specific ATC value will be selected based on a number of social, economic, deregulated environmental constraints and related to specific applications like optimization, on-line monitoring, and ATC forecasting known as multi-objective decision based optimal ATC. The validity of results obtained through these proposed methods are scrupulously verified on various buses of the IEEE 24-bus reliable test system. The results presented and derived conclusions in this paper are very useful for planning, operation, maintaining of reliable power in any power system and its monitoring in an on-line environment of deregulated power system. In this way, the proposed heuristic methods would contribute the best possible approach to assess multiple objective ATC using integrated methods.
A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke.
Roy, S; Cheng, M; Chang, S; Moore, J; De Luca, G; Nawab, S; De Luca, C
2014-04-23
Remote monitoring of physical activity using bodyworn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data were recorded from 10 hemi paretic patients while they carried out a sequence of 11 activities of daily living (Identification tasks), and 10 activities used to evaluate misclassification errors (non-Identification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the non-Identification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of 4 ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0 %, and a mean specificity of 99.7 % for the identification tasks, and a mean misclassification error of < 10% for the non-Identification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. PMID:24760921
A combined sEMG and accelerometer system for monitoring functional activity in stroke.
Roy, Serge H; Cheng, M Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S Hamid; De Luca, Carlo J
2009-12-01
Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of < 10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. PMID:20051332
Hybrid soft computing systems for reservoir PVT properties prediction
NASA Astrophysics Data System (ADS)
Khoukhi, Amar
2012-07-01
In reservoir engineering, the knowledge of Pressure-Volume-Temperature (PVT) properties is of great importance for many uses, such as well test analyses, reserve estimation, material balance calculations, inflow performance calculations, fluid flow in porous media and the evaluation of new formations for the potential development and enhancement oil recovery projects. The determination of these properties is a complex problem because laboratory-measured properties of rock samples ("cores") are only available from limited and isolated well locations and/or intervals. Several correlation models have been developed to relate these properties to other measures which are relatively abundant. These models include empirical correlations, statistical regression and artificial neural networks (ANNs). In this paper, a comprehensive study is conducted on the prediction of the bubble point pressure and oil formation volume factor using two hybrid of soft computing techniques; a genetically optimised neural network and a genetically enhanced subtractive clustering for the parameter identification of an adaptive neuro-fuzzy inference system. Simulation experiments are provided, showing the performance of the proposed techniques as compared with commonly used regression correlations, including standard artificial neural networks.
Use of Learning Classifier System for Inferring Natural Language Grammar
Olgierd Unold; Grzegorz Dabrowski
2005-01-01
This paper deals with the use of learning classifier system—LCS—for inferring a nontrivial natural language grammar. In a\\u000a repeated analysis LCS infers the grammar of a given natural language from an exemplary set of correct and incorrect sentences.\\u000a A genetic algorithm used periodically strengthens LCS’s operation. A context-free grammar is used in the description of language\\u000a structure.
INFeRS: Interactive Numeric Files Retrieval System. Final Report.
ERIC Educational Resources Information Center
Chiang, Katherine; And Others
In 1988 Mann Library at Cornell University proposed to develop a computer system that would support interactive access to significant electronic files in agriculture and the life sciences. This system was titled the Interactive Numeric Files Retrieval System (INFeRS). This report describes how project goals were met and it presents the project's…
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.
A type declaration and inference system for smalltalk
Alan H. Borning; Daniel H. H. Ingalls
1982-01-01
An experimental system for declaring and inferring type in Smalltalk is described. (In the current Smalltalk language, the programmer supplies no type declarations.) The system provides the benefits of type declaration in regard to compile-time checking and documentation, while still retaining Smalltalk's flexibility. A type hierarchy, which is integrated with the existing Smalltalk class hierarchy, allows one type to inherit
Fuzzy inference to risk assessment on nuclear engineering systems
Antonio César Ferreira Guimarães; Celso Marcelo Franklin Lapa
2007-01-01
This paper presents a nuclear case study, in which a fuzzy inference system (FIS) is used as alternative approach in risk analysis. The main objective of this study is to obtain an understanding of the aging process of an important nuclear power system and how it affects the overall plant safety. This approach uses the concept of a pure fuzzy
IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-9 RITE
Chen, Sheng-Wei
IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-9 RITE Min-Yuh Day 1 inference in text at NTCIR-9 RITE (Recognizing Inference in Text). We proposed a textual entailment system inference in text at NTCIR-9 RITE task. We submitted 3 official runs for both BC and MC subtask. In NTCIR-9
On Inference Rules of Logic-Based Information Retrieval Systems.
ERIC Educational Resources Information Center
Chen, Patrick Shicheng
1994-01-01
Discussion of relevance and the needs of the users in information retrieval focuses on a deductive object-oriented approach and suggests eight inference rules for the deduction. Highlights include characteristics of a deductive object-oriented system, database and data modeling language, implementation, and user interface. (Contains 24…
Predictions not commands: active inference in the motor system.
Adams, Rick A; Shipp, Stewart; Friston, Karl J
2013-05-01
The descending projections from motor cortex share many features with top-down or backward connections in visual cortex; for example, corticospinal projections originate in infragranular layers, are highly divergent and (along with descending cortico-cortical projections) target cells expressing NMDA receptors. This is somewhat paradoxical because backward modulatory characteristics would not be expected of driving motor command signals. We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz's ideas about perception; namely, that perception is inference on the causes of visual sensations. We explain behaviour in terms of inference on the causes of proprioceptive sensations. This explanation appeals to active inference, in which higher cortical levels send descending proprioceptive predictions, rather than motor commands. This process mirrors perceptual inference in sensory cortex, where descending connections convey predictions, while ascending connections convey prediction errors. The anatomical substrate of this recurrent message passing is a hierarchical system consisting of functionally asymmetric driving (ascending) and modulatory (descending) connections: an arrangement that we show is almost exactly recapitulated in the motor system, in terms of its laminar, topographic and physiological characteristics. This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular. PMID:23129312
A knowledge-based expert system for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Kimes, Daniel S.; Harrison, Patrick R.; Ratcliffe, P. A.
1991-01-01
A prototype knowledge-based expert system VEG is presented that focuses on extracting spectral hemispherical reflectance using any combination of nadir and/or directional reflectance data as input. The system is designed to facilitate expansion to handle other inferences regarding vegetation properties such as total hemispherical reflectance, leaf area index, percent ground cover, phosynthetic capacity, and biomass. This approach is more robust and accurate than conventional extraction techniques previously developed.
An expert system shell for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1992-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown cover type data has been constructed. An interface that allows the results of VEG to be written to a file has been implemented. A learning system that learns class descriptions from a data base of historical cover type data and then uses the learned class descriptions to classify an unknown sample has been built. This system has an interface that integrates it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented.
Efficient parametric inference for stochastic biological systems with measured variability.
Johnston, Iain G
2014-06-01
Stochastic systems in biology often exhibit substantial variability within and between cells. This variability, as well as having dramatic functional consequences, provides information about the underlying details of the system's behavior. It is often desirable to infer properties of the parameters governing such systems given experimental observations of the mean and variance of observed quantities. In some circumstances, analytic forms for the likelihood of these observations allow very efficient inference: we present these forms and demonstrate their usage. When likelihood functions are unavailable or difficult to calculate, we show that an implementation of approximate Bayesian computation (ABC) is a powerful tool for parametric inference in these systems. However, the calculations required to apply ABC to these systems can also be computationally expensive, relying on repeated stochastic simulations. We propose an ABC approach that cheaply eliminates unimportant regions of parameter space, by addressing computationally simple mean behavior before explicitly simulating the more computationally demanding variance behavior. We show that this approach leads to a substantial increase in speed when applied to synthetic and experimental datasets. PMID:24821877
Edinburgh Research Explorer Combining tree-based and dynamical systems for the inference
Millar, Andrew J.
an important open problem in computational systems biology. Existing GRN inference algorithms face one of two-based and dynamical systems for the inference of gene regulatory networks' Bioinformatics, vol 31, no. 10, pp. 1614. 2015 #12;Systems biology Combining tree-based and dynamical systems for the inference of gene
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.
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.
How to Interpret Neural Networks In Terms of Fuzzy Logic?
Kreinovich, Vladik
How to Interpret Neural Networks In Terms of Fuzzy Logic? Sompong Dhompongsa 1 , Vladik Kreinovich the description of a neural network in terms of fuzzy logic. Such a description is also very important to better understand efficient neurofuzzy systems such as the Adaptive NetworkBased Fuzzy Inference System AN FIS
Erratum to Central European Journal of Engineering, Volume 4, Issue 1
NASA Astrophysics Data System (ADS)
Kumar, M.; Srikanth, N.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
Erratum: Erratum to Central European Journal of Engineering, Volume 4, Issue 1
NASA Astrophysics Data System (ADS)
Kumar, M. Ajay; Srikanth, N. V.
2014-06-01
Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below
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
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
A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)
NASA Technical Reports Server (NTRS)
Truong, Son H.
1999-01-01
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.
Kuehnlenz, Kolja
Learning, Spiking Neural Networks, Neuro- Fuzzy, Spike Time Dependent Plasticity, Cart-Pole balancing. 1 Publishing Switzerland 2014 Sensorimotor Control Learning Using a New Adaptive Spiking Neuro-Fuzzy Machine an adaptive Neuro-Fuzzy machine in order to model a rule-based MIMO system as logical combination of spatially
Joo, Su-Chong
Heidelberg 2006 Location-Aware Data Mining for Mobile Users Based on Neuro-fuzzy System* Romeo Mark A. Mateo1 or uncertain informa- tion. This paper proposes a neuro-fuzzy data mining approach which provides a means and uses the neuro-fuzzy algorithm for data min- ing. It also introduces the user-profile frequency count
A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)
NASA Technical Reports Server (NTRS)
Truong, S. H.
1999-01-01
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.
Automatic generation of fuzzy inference systems via unsupervised learning.
Er, Meng Joo; Zhou, Yi
2008-12-01
In this paper, a novel approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating Fuzzy Inference Systems (FISs) is presented. In the EDSGFQL approach, structure identification and parameter estimations of FISs are achieved via Unsupervised Learning (UL) (including Reinforcement Learning (RL)). Instead of using Supervised Learning (SL), UL clustering methods are adopted for input space clustering when generating FISs. At the same time, structure and preconditioning parts of a FIS are generated in a RL manner in that fuzzy rules are adjusted and deleted according to reinforcement signals. The proposed EDSGFQL methodologies can automatically create, delete and adjust fuzzy rules dynamically. Simulation studies on wall-following and obstacle avoidance tasks by a mobile robot show that the proposed approach is superior in generating efficient FISs. PMID:18653313
FUZZY INFERENCE SYSTEM FOR PIOPED-COMPLIANT DIAGNOSIS OF PULMONARY EMBOLISM
Serpen, Gursel
FUZZY INFERENCE SYSTEM FOR PIOPED-COMPLIANT DIAGNOSIS OF PULMONARY EMBOLISM G. Serpen1 , Ph. D., V USA ABSTRACT A Fuzzy Inference System was developed to aid in the diagnosis of Pulmonary Embolism. The initial attempts to diagnose for Pulmonary Embolism employed Knowledge Based Systems [Burton et al. 1984
Development of a rule-based inference model for human sensibility engineering system
Sun-Mo Yang; Beumjun Ahn; Kwang-Kyu Seo
2005-01-01
Human Sensibility Engineering System (HSES) has been applied to product development for customer’ s satisfaction based on\\u000a ergonomic technology The system is composed of thiee parts such as human sensibility analysis, inference mechanism, and piesentatton\\u000a technologies Inference mechanism translating human sensibility into design elements plays an important role in the HSES In\\u000a this paper, we propose a rule-based inference model
Bayesian Inference Bayesian Inference
Penny, Will
Bayesian Inference Will Penny Bayesian Inference Bayes rule Medical Decision Making Directed Gaussians Sensory Integration Decision Making Dynamics References Bayesian Inference Will Penny 24th February 2011 #12;Bayesian Inference Will Penny Bayesian Inference Bayes rule Medical Decision Making
Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
2009-04-01
Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.
Chow, Mo-Yuen
of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault de presents two neural fuzzy (NN/FZ) inference systems, namely, Fuzzy Adaptive Learning Control/Decision Network (FALCON) and Adaptive Network Based Fuzzy Inference System (ANFIS), with applications to induction
A primer on Bayesian inference for biophysical systems.
Hines, Keegan E
2015-05-01
Bayesian inference is a powerful statistical paradigm that has gained popularity in many fields of science, but adoption has been somewhat slower in biophysics. Here, I provide an accessible tutorial on the use of Bayesian methods by focusing on example applications that will be familiar to biophysicists. I first discuss the goals of Bayesian inference and show simple examples of posterior inference using conjugate priors. I then describe Markov chain Monte Carlo sampling and, in particular, discuss Gibbs sampling and Metropolis random walk algorithms with reference to detailed examples. These Bayesian methods (with the aid of Markov chain Monte Carlo sampling) provide a generalizable way of rigorously addressing parameter inference and identifiability for arbitrarily complicated models. PMID:25954869
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris
2013-01-01
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245
An integrated fuzzy inference based monitoring, diagnostic, and prognostic system
NASA Astrophysics Data System (ADS)
Garvey, Dustin
To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable "grab bag" of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ˜45 hours and 95% CIs of ˜162 hours.
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
Jo Bovy; Iain Murray; David W. Hogg
2010-02-24
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_r = -A [r/r_0]^{-\\alpha}, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < \\alpha < 2.052 (95 percent 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.
A Diagrammatic Inference System for the Web Michael Wollowski
Wollowski, Michael
5500 Wabash Ave. Terre Haute, IN 47803, USA 001-812-877-8650 wollowski@rose-hulman.edu Peter Nei1 Beckman Coultier., PDC 7451 Winton Drive Indianapolis, IN 46268, USA 001-317-808-4382 peter and inference engine. If done right, this makes the information easier to read for people [2]. However
From free energy measurements to free energy inference in small systems Felix Ritort
Potsdam, Universität
From free energy measurements to free energy inference in small systems the knowledge of the free energy of nucleic acid and protein structures the free energy of pure equilibrium states, FTs have been extended
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.
Controlling a drone: Comparison between a based model method and a fuzzy inference system
Kadda Meguenni Zemalache; Hichem Maaref
2009-01-01
The work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi–Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of
Nonparametric predictive inference for voting systems F.P.A. Coolen
Coolen, Frank
that inferences on system reliability are based on information from tests on n components, which are exchangeable upper and lower probabilities for reliability of voting systems, also known as k-out- of-m systems for reliability of voting systems. 1 Introduction During the last decade, imprecise probabilistic methods
Data Quality in ANFIS Based Soft Sensors
S. Jassar; Z. Liao; L. Zhao
\\u000a Soft sensor are used to infer the critical process variables that are otherwise difficult, if not impossible, to measure in\\u000a broad range of engineering fields. Adaptive Neuro-Fuzzy Inference System (ANFIS) has been employed to develop successful ANFIS\\u000a based inferential model that represents the dynamics of the targeted system. In addition to the structure of the model, the\\u000a quality of the
Cut Elimination inside a Deep Inference System for Classical Predicate Logic
Kai Brünnler
2006-01-01
Deep inference is a natural generalisation of the one-sided sequent calculus where rules are allowed to apply deeply inside\\u000a formulas, much like rewrite rules in term rewriting. This freedom in applying inference rules allows to express logical systems\\u000a that are difficult or impossible to express in the cut-free sequent calculus and it also allows for a more fine-grained analysis\\u000a of
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.
An evolutionary approach toward dynamic self-generated fuzzy inference systems.
Zhou, Yi; Er, Meng Joo
2008-08-01
An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods. PMID:18632385
A COMPUTATIONAL MODEL FOR CAUSAL AND DIAGNOSTIC REASONING IN INFERENCE SYSTEMS Jin H. Kirn2
California at Los Angeles, University of
in addition to the ususal interaction among diagnostic indicators. This paper is organized as follows. AfterA COMPUTATIONAL MODEL FOR CAUSAL AND DIAGNOSTIC REASONING IN INFERENCE SYSTEMS Jin H. Kirn2 and Judea Pearl Cognitive Systems Laboratory University of California, Los Angeles ABSTRACT This paper
An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1992-01-01
This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.
Inferring superposition and entanglement in evolving systems from measurements in a single basis
Schelpe, Bella; Kent, Adrian; Munro, William; Spiller, Tim
2003-05-01
We discuss what can be inferred from measurements on evolving one- and two-qubit systems using a single measurement basis at various times. We show that, given reasonable physical assumptions, carrying out such measurements at quarter-period intervals is enough to demonstrate coherent oscillations of one or two qubits between the relevant measurement basis states. One can thus infer from such measurements alone that an approximately equal superposition of two measurement basis states has been created during a coherent oscillation experiment. Similarly, one can infer that a near-maximally entangled state of two qubits has been created part way through an experiment involving a putative SWAP gate. These results apply even if the relevant quantum systems are only approximate qubits. We discuss applications to fundamental quantum physics experiments and quantum-information processing investigations.
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 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.
Bayesian Inference Networks and Spreading Activation in Hypertext Systems.
ERIC Educational Resources Information Center
Savoy, Jacques
1992-01-01
Describes a method based on Bayesian networks for searching hypertext systems. Discussion covers the use of Bayesian networks for structuring index terms and representing user information needs; use of link semantics based on constrained spreading activation to find starting points for browsing; and evaluation of a prototype system. (64…
Seizure detection in intracranial EEG using a fuzzy inference system
A. Aarabi; R. Fazel-Rezai; Y. Aghakhani
2009-01-01
In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from
Fuzzy inference system for evaluating and improving nuclear power plant operating performance
Antonio César F. Guimarães; Celso Marcelo Franklin Lapa
2003-01-01
This paper presents a fuzzy inference system (FIS) as an approach to estimate Nuclear Power Plant (NPP) performance indicators. The performance indicators for this study are the energy availability factor (EAF) and the planned (PUF) and unplanned unavailability factor (UUF). These indicators are obtained from a non analytical combination among the same operational parameters. Such parameters are, for example, environment
Inferring prey perception of relative danger in large-scale marine systems
Alejandro Frid; Lawrence M. Dill; Richard E. Thorne; Gail M. Blundell
2007-01-01
Problem: Infer relative danger from spatially segregated predators in large-scale marine systems, using individual differences in prey foraging behaviour. Mathematical models: Optimization of trade-offs between predation risk and energy gain. Key assumption: Foraging individuals choosing to incur higher risk of predation should experience higher energetic gain than individuals choosing safer foraging options. Organisms: Alaskan harbour seals foraging under predation risk
FPGA-Based Fuzzy Inference System for Real-time Embedded Applications
to control a PM motor in a washing machine. The given results demonstrate the capability of such embedded issues. Keywords:- FPGA, Fuzzy logic, Fuzzy inference system, PM motor, Washing machine. INTRODUCTION) on an FPGA. The implemented FPGA- based FIS has been used to control the operation of a permanent magnet (PM
Parameter optimization of a Fuzzy Inference System using the FisPro open source software
Paris-Sud XI, Université de
Parameter optimization of a Fuzzy Inference System using the FisPro open source software Serge that they accept semantic constraints. The optimization sequence is implemented in an open source software, Fis of the developments are implemented in an open source software called FisPro1 , which is used to illustrate
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.
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.'
Web Enabled Expert Systems Using Hyperlink Based Inference
Yong Uk Song; Wooju Kim; June Seok Hong
2003-01-01
Abstract With the proliferation of the WWW, providing more intelligent Websites has become a major concern in the e-business industry. Recently, this trend has been,even,more,accelerated by,the success of Customer,Relationship Management,(CRM) in terms of product recommendation, and self after service, etc. As a result, many e-companies are eager to embed Web-enabled, rule-based systems, i.e. that is, expert systems, into their Websites,
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
Web enabled expert systems using hyperlink-based inference
Wooju Kim; Yong Uk Song; June Seok Hong
2005-01-01
With the proliferation of the WWW, providing more intelligent Websites has become a major concern in the e-business industry. Recently, this trend has been even more accelerated by the success of Customer Relationship Management (CRM) in terms of product recommendation, and self after service, etc. As a result, many e-companies are eager to embed Web-enabled, rule-based systems, i.e. that is,
Web enabled expert systems using hyperlink-based inference
Wooju Kim; Yong U. Song; June S. Hong
With the proliferation of the WWW, providing more intelligent Websites has become a major concern in the e-business industry. Recently, this trend has been even more accelerated by the success of Customer Relationship Management (CRM) in terms of product recommendation, and self after service, etc. As a result, many e-companies are eager to embed Web-enabled, rule-based systems, i.e. that is,
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.
2010-01-01
Background Reaction-diffusion systems are frequently used in systems biology to model developmental and signalling processes. In many applications, count numbers of the diffusing molecular species are very low, leading to the need to explicitly model the inherent variability using stochastic methods. Despite their importance and frequent use, parameter estimation for both deterministic and stochastic reaction-diffusion systems is still a challenging problem. Results We present a Bayesian inference approach to solve both the parameter and state estimation problem for stochastic reaction-diffusion systems. This allows a determination of the full posterior distribution of the parameters (expected values and uncertainty). We benchmark the method by illustrating it on a simple synthetic experiment. We then test the method on real data about the diffusion of the morphogen Bicoid in Drosophila melanogaster. The results show how the precision with which parameters can be inferred varies dramatically, indicating that the ability to infer full posterior distributions on the parameters can have important experimental design consequences. Conclusions The results obtained demonstrate the feasibility and potential advantages of applying a Bayesian approach to parameter estimation in stochastic reaction-diffusion systems. In particular, the ability to estimate credibility intervals associated with parameter estimates can be precious for experimental design. Further work, however, will be needed to ensure the method can scale up to larger problems. PMID:20219114
Fuzzy Control Strategies in Human Operator and Sport Modeling
Ivancevic, Tijana T; Markovic, Sasa
2009-01-01
The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.
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.
Prediction of chaotic time series using computational intelligence
B. Samanta
2011-01-01
In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANFIS has been illustrated using
Iman Azarkhish; Mohammad Reza Raoufy; Shahriar Gharibzadeh
Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming,\\u000a expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb\\/RBC),\\u000a we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and\\u000a to predict serum iron
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
A novel bridge scour monitoring and prediction system
NASA Astrophysics Data System (ADS)
Valyrakis, Manousos; Michalis, Panagiotis; Zhang, Hanqing
2015-04-01
Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the use of a novel methodology is proposed for the prediction of bridge scour. Specifically, the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation. Training of the system to new bridge geometries and flow conditions can be achieved by obtaining real time data, via novel electromagnetic sensors monitoring scour depth. Once the model is trained with data representative of the new system, bridge scour prediction can be performed for high/design flows or floods.
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.
Assessing water quality in rivers with fuzzy inference systems: a case study.
Ocampo-Duque, William; Ferré-Huguet, Núria; Domingo, José L; Schuhmacher, Marta
2006-08-01
In recent years, fuzzy-logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental problems. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. A water quality index calculated with fuzzy reasoning has been developed. The relative importance of water quality indicators involved in the fuzzy inference process has been dealt with a multi-attribute decision-aiding method. The potential application of the fuzzy index has been tested with a case study. A data set collected from the Ebro River (Spain) by two different environmental protection agencies has been used. The current findings, managed within a geographic information system, clearly agree with official reports and expert opinions about the pollution problems in the studied area. Therefore, this methodology emerges as a suitable and alternative tool to be used in developing effective water management plans. PMID:16678900
From free energy measurements to thermodynamic inference in nonequilibrium small systems
NASA Astrophysics Data System (ADS)
Alemany, A.; Ribezzi-Crivellari, M.; Ritort, F.
2015-07-01
Fluctuation theorems (FTs), such as the Crooks or Jarzynski equalities (JEs), have become an important tool in single-molecule biophysics where they allow experimentalists to exploit thermal fluctuations and measure free-energy differences from non-equilibrium pulling experiments. The rich phenomenology of biomolecular systems has stimulated the development of extensions to the standard FTs, to encompass different experimental situations. Here we discuss an extension of the Crooks fluctuation relation that allows the thermodynamic characterization of kinetic molecular states. This extension can be connected to the generalized JE under feedback. Finally we address the recently introduced concept of thermodynamic inference or how FTs can be used to extract the total entropy production distribution in nonequilibrium systems from partial entropy production measurements. We discuss the significance of the concept of effective temperature in this context and show how thermodynamic inference provides a unifying comprehensive picture in several nonequilibrium problems.
INRECA: A Seamlessly Integrated System Based on Inductive Inference and Case-Based Reasoning
Eric Auriol; Stefan Wess; Michel Manago; Klaus-dieter Althoff; Ralph Traphöner
1995-01-01
This paper focuses on integrating inductive inference and case-based reasoning. We study integration along two dimensions: Integration of case-based methods with methods based on general domain knowledge, and integration of problem solving and incremental learning from experience. In the INRECA system, we perform case-based reasoning as well as TDIDT (Top- Down Induction of Decision Trees) classification by using the same
A Neuro-Fuzzy Multi Swarm FastSLAM Framework
Havangi, R; Nekoui, M A
2010-01-01
FastSLAM is a framework for simultaneous localization using a Rao-Blackwellized particle filter. In FastSLAM, particle filter is used for the mobile robot pose (position and orientation) estimation, and an Extended Kalman Filter (EKF) is used for the feature location's estimation. However, FastSLAM degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot loses its diversity. One of the main reasons for loosing particle diversity in FastSLAM is sample impoverishment. It occurs when likelihood lies in the tail of the proposal distribution. In this case, most of particle weights are insignificant. Another problem of FastSLAM relates to the design of an extended Kalman filter for landmark position's estimation. The performance of the EKF and the quality of the estimation depends heavily on correct a priori knowledge of the process and measurement noise covariance matrices (Q and R) that are in most applications unknown. On the other hand, an incorrect a priori...
A Neuro-Fuzzy Approach for Medical Image Fusion.
Das, Sudeb; Kundu, Malay Kumar
2013-12-01
This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled 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 the RPCNN with a less complex structure and having less number of parameters leads to computational efficiency-an important requirement of point-of-care 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 the existing techniques. PMID:24058012
Classification of delaminated composites using neuro-fuzzy image analysis
Martin, Ralph R.
characteristics that are obvious to human visual inspection. In this paper, we present a technique that combines conventional image analysis, neural networks and fuzzy decision-making. The motivation for this approach inspection. In this paper we present an application com- bining conventional image analysis, neural and fuzzy
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
Wissensverarbeitung Inference-Inference -
knowledge and knowledge iprocessing Knowledge management i.e. conventional toolsg g (data bases) Outstanding role of logic and inferenceOutstanding role of logic and inference Logic for KR I f f d i ti f (WME) is a concreteg y ( ) instantiation WME comprisesWME comprises Names Attributes Attributes
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
A new Multiple ANFIS model for classification of hemiplegic gait.
Yardimci, A; Asilkan, O
2014-01-01
Neuro-fuzzy system is a combination of neural network and fuzzy system in such a way that neural network learning algorithms, is used to determine parameters of the fuzzy system. This paper describes the application of multiple adaptive neuro-fuzzy inference system (MANFIS) model which has hybrid learning algorithm for classification of hemiplegic gait acceleration (HGA) signals. Decision making was performed in two stages: feature extraction using the wavelet transforms (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the HGA signals. PMID:25160151
Aggelopoulos, Nikolaos C
2015-08-01
Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. PMID:25976632
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.
Inference of gene regulatory networks using S-system: a unified approach.
Wang, H; Qian, L; Dougherty, E
2010-03-01
With the increased availability of DNA microarray time-series data, it is possible to discover dynamic gene regulatory networks (GRNs). S-system is a promising model to capture the rich dynamics of GRNs. However, owing to the complexity of the inference problem and limited number of available data comparing to the number of unknown kinetic parameters, S-system can only be applied to a very small GRN with few parameters. This significantly limits its applications. A unified approach to infer GRNs using the S-system model is proposed. In order to discover the structure of large-scale GRNs, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using genetic programming and recursive least square estimation. Then the mean values of the parameters will be estimated using a multi-dimensional optimisation algorithm. Both the downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimisation. A 50-dimensional synthetic model with 51 parameters for each gene is tested for the applicability of the simplified S-system model. In addition, real measurement data pertaining to yeast protein synthesis are used to demonstrate the effectiveness of the proposed two-step method to identify the detailed interactions among genes in small GRNs. PMID:20232994
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
2013-01-01
Background Model selection and parameter inference are complex problems that have yet to be fully addressed in systems biology. In contrast with parameter optimisation, parameter inference computes both the parameter means and their standard deviations (or full posterior distributions), thus yielding important information on the extent to which the data and the model topology constrain the inferred parameter values. Results We report on the application of nested sampling, a statistical approach to computing the Bayesian evidence Z, to the inference of parameters, and the estimation of log Z in an established model of circadian rhythms. A ten-fold difference in the coefficient of variation between degradation and transcription parameters is demonstrated. We further show that the uncertainty remaining in the parameter values is reduced by the analysis of increasing numbers of circadian cycles of data, up to 4 cycles, but is unaffected by sampling the data more frequently. Novel algorithms for calculating the likelihood of a model, and a characterisation of the performance of the nested sampling algorithm are also reported. The methods we develop considerably improve the computational efficiency of the likelihood calculation, and of the exploratory step within nested sampling. Conclusions We have demonstrated in an exemplar circadian model that the estimates of posterior parameter densities (as summarised by parameter means and standard deviations) are influenced predominately by the length of the time series, becoming more narrowly constrained as the number of circadian cycles considered increases. We have also shown the utility of the coefficient of variation for discriminating between highly-constrained and less-well constrained parameters. PMID:23899119
Hanemaaijer, Mark; Röling, Wilfred F. M.; Olivier, Brett G.; Khandelwal, Ruchir A.; Teusink, Bas; Bruggeman, Frank J.
2015-01-01
Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist. PMID:25852671
Hanemaaijer, Mark; Röling, Wilfred F M; Olivier, Brett G; Khandelwal, Ruchir A; Teusink, Bas; Bruggeman, Frank J
2015-01-01
Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call "the community state", that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist. PMID:25852671
Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo
This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.
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.
GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.
Tung, W L; Quek, C; Cheng, P
2004-05-01
Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging. PMID:15109685
A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference.
Chiang, Tzu-Chiang; Liang, Wen-Hua
2015-09-01
In the present society, most families are double-income families, and as the long-term care is seriously short of manpower, it contributes to the rapid development of tele-homecare equipment, and the smart home care system gradually emerges, which assists the elderly or patients with chronic diseases in daily life. This study aims at interaction between persons under care and the system in various living spaces, as based on motion-sensing interaction, and the context-aware smart home care system is proposed. The system stores the required contexts in knowledge ontology, including the physiological information and environmental information of the person under care, as the database of decision. The motion-sensing device enables the person under care to interact with the system through gestures. By the inference mechanism of fuzzy theory, the system can offer advice and rapidly execute service, thus, implementing the EHA. In addition, the system is integrated with the functions of smart phone, tablet PC, and PC, in order that users can implement remote operation and share information regarding the person under care. The health care system constructed in this study enables the decision making system to probe into the health risk of each person under care; then, from the view of preventive medicine, and through a composing system and simulation experimentation, tracks the physiological trend of the person under care, and provides early warning service, thus, promoting smart home care. PMID:26265236
Another expert system rule inference based on DNA molecule logic gates
NASA Astrophysics Data System (ADS)
WÄ siewicz, Piotr
2013-10-01
With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.
1986-01-01
This paper summarizes the modifications made to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of an update frequency, are also presented.
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.
Soft Computing Paradigms for Hybrid Fuzzy Controllers: Experiments and applications*
Fernandez, Thomas
with the aid of these soft computing paradigms are presented. 2 Neuro-Fuzzy System Neural networks exhibit model. The neuro-fuzzy control architec- ture uses the two neural networks to modify the param- eters) University of New Mexico, Albuquerque, NM 87131, USA E-mail: akbazar@eece.unm.edu Abstract Neural Networks
Huifang Shi; Wenhua Song; Qinggong Li
2010-01-01
The project in petrochemical industry is complex and risky. For this feature, we established a safety risk assessment (SRA) expert system based on fault tree in petrochemical industry. In this paper, we studied the design and implementation of infer engine in SRA expert system. we adopted the method of fault tree analysis (FTA) to acquire expert knowledge and the fault
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.
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.
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.
Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Toro, Luis; Cavazos, Alberto; Colás, Rafael
2009-12-01
A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.
NASA Astrophysics Data System (ADS)
Wilting, Jens; Lehnertz, Klaus
2015-08-01
We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.
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.
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…
Henke, Joseph D
2014-01-01
Common Sense Inference is an increasingly attractive technique to make computer interfaces more in touch with how human users think. However, the results of the inference process are often hard to interpret and evaluate. ...
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.
Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.
Azar, Ahmad Taher
2013-09-01
Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.
1985-01-01
This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.
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.
A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease.
Camara, Carmen; Warwick, Kevin; Bruña, Ricardo; Aziz, Tipu; Del Pozo, Francisco; Maestú, Fernando
2015-11-01
Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients. PMID:26385550
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
Inferring the Architectures of Planetary Systems from Kepler Results with SysSim
NASA Astrophysics Data System (ADS)
Ford, Eric
NASA's Kepler Mission is designed to gather statistical information about planets around other stars (exoplanets). Due to its unprecedented precision, Kepler has revolutionized the exoplanet community providing by far the largest homogeneous exoplanet dataset, with over 2300 planet candidates already identified in the first 19 months of data (Batalha et al. 2012, hereafter B12). Even more exciting is Kepler's new discovery of "multi-transiting systems" or stars with more than one transiting planet. B12 identified over 350 multi-transiting systems containing nearly 900 high-probability candidate planets (Lissauer et al. 2012). These multi-transiting systems are the most information-rich planetary systems outside our own solar system because they offer both the detailed physical characterization uniquely available from transiting planets (Winn 2010) and the ability to apply the tools of multi- planetary orbital dynamics (Ragozzine & Holman 2010, Lissauer, Ragozzine et al. 2011, hereafter LR+11). Within the funding period, publicly-available Kepler observations will triple in duration, yielding hundreds of new planets in multi-transiting systems and providing first insights into the frequency of Earth-size planets and solar system analogs. As with all astronomical surveys, Kepler data must be debiased in order to understand the true properties of the underlying population. Several studies have addressed the geometrical and detection biases to perform statistical analyses of Kepler results on a planet-by-planet basis (e.g., Borucki et al. 2011, Howard et al. 2011, Youdin 2011, Catanzarite & Shao 2011, Traub 2011). Other authors have analyzed specific multi- transiting systems to measure physical and orbital properties that will inform planet formation theories (e.g., Lissauer et al. 2011a, Migaszewski et al. 2012). However, there is a critical gap between these two techniques: a method for debiasing the Kepler planet data while accounting for multi-planet systems. Without a method to interpret and debias the Kepler planet candidates on a system-by- system basis, it is not possible to rigorously address critical NASA-relevant science questions like: 1) What fraction of stars have planets? What fraction of stars have solar system analogs? 2) What is the planetary system environment of potentially habitable planets? 3) What is the expected yield of future NASA exoplanet missions? 4) Are there different populations of planetary systems? What are their architectures? and many other valuable questions that are critical for understanding the origins of solar systems. To fill this critical gap, we have developed the Planetary System Simulator or SysSim, which empirically determines the underlying debiased distribution of planetary properties (e.g., planet size, orbital period, etc.) and planetary system architecture (e.g., relative inclinations, number of planets per star) simultaneously. The earliest version of SysSim measured the exoplanetary inclination distribution for the first time, a finding of major consequence for planet formation theorists (LR+11). We propose to extend SysSim to include new planetary architecture parameters and new observational constraints from the growing Kepler dataset. We will produce rigorously-debiased exoplanetary populations that will improve the understanding of the frequency, architecture, and origins of planetary systems. Our team is uniquely qualified to fulfill these tasks based on our extensive experience with Kepler data, metadata, multi-transiting systems, statistical inference, and previous work (LR+11, B12, Ragozzine & Holman 2010).
Jun-Ki Min; Sung-Bae Cho
\\u000a Multiple sensor-based context inference systems can perceive users’ tasks in detail while it requires complicated recognition\\u000a models with larger resources. Such limitations make the systems difficult to be used for the mobile environment where the\\u000a context-awareness would be most needed. In order to design and operate the complex models efficiently, this paper proposes\\u000a an evolutionary process for generating the context
J. Enrique Muñoz Expósito; Sebastian García Galán; Nicolás Ruiz-Reyes; Pedro Vera-Candeas
2007-01-01
Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents an effective approach based on an adaptive network-based fuzzy inference system (ANFIS) for the classification stage required in a speech\\/music discrimination system. A new simple feature, called warped LPC-based spectral centroid (WLPC-SC), is also proposed. Comparison between WLPC-SC and the classical features proposed
A Cloud Trust Evaluation System using Hierarchical Fuzzy Inference System for Service Selection
Buyya, Rajkumar
the effectiveness and efficiency of our system through simulations and case studies. Keywords--Cloud Computing Chenhao Qu and Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Lab Department of Computing and Information Systems The University of Melbourne, Australia Email: cqu@student
Tfwala, Samkele S; Wang, Yu-Min; Lin, Yu-Chieh
2013-01-01
Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R (2) of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM. PMID:24453876
Du, Yi-Chun; Lin, Chia-Hung
2012-02-01
Detecting lower limb peripheral vascular occlusive disease (PVOD) early is important for patients to prevent disabling claudication, ischaemic rest pain and gangrene. According to previous research, the pulse timing and shape distortion characteristics of photoplethysmography (PPG) signals tend to increase with disease severity and calibrated amplitude decreases with vascular diseases. However, this is not a reliable method of evaluating the condition of PVOD because of noise effect. In this paper, an adaptive network-based fuzzy inference system (ANFIS) is proposed to assess lower limb PVOD based on PPG signals. PPG signals are non-invasively recorded from the right and left sides at the big toe sites from twenty subjects, including normal condition (Nor), lower-grade disease (LG), and higher-grade disease (HG) groups. The number of each group is 10, 8 and 2 respectively, and the ages ranged from 24 to 65 years. With the time-domain technique, the parameters for the absolute bilateral differences (right-to-left side of foot) in pulse delay and amplitude were extracted for analyzing ANFIS. The results indicated that ANFIS based on three timing parameters base bilateral differences, including ?PTTf and ?PTTp, and ?RT has a high rate and noise tolerance of PVOD assessment. PMID:20703718
User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.
1988-01-01
Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.
Misra, Sudip; Singh, Ranjit; Rohith Mohan, S V
2010-01-01
The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307
Van Diest, P J; Beliën, J A; Zanstra, P E; Wilhelm, W W; Baak, J P
1994-09-01
Histological typing of invasive breast cancer according to the World Health Organisation criteria is prognostically relevant, because some histological subtypes have a markedly better prognosis. However, reproducibility of histological typing is not high because of the absence of strict typing criteria, variations in the application of the typing criteria and the usually limited illustration of the relevant criteria. The aim of this study was to develop an expert system based on highly structured histological typing criteria, integrated with high-quality microscope images to illustrate the typing criteria. This system should be useful as a decision support system in the diagnosis of breast cancers and should increase the reproducibility of histological typing. Criteria for typing were extracted from textbooks and, based on experience, these criteria were structured and implemented in the Relation Oriented Inference System (ROIS), in which information can be structured by defining relations. Illustrative black and white images were digitized and integrated into the shell. The performance of the resulting decision support system was evaluated by a group of six pathologists using a set of slides covering the spectrum of the most frequently occurring histological types of invasive breast cancer. The pathologists first assessed histological type according to standard morphological procedures. The cases were then reassessed with the decision support system available for consultation. The use of the decision support system appeared to influence the previously assessed histological type in about half of the cases. Using the decision support system, histological typing was more uniform and more in accord with a 'gold standard' set by two experts.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7821893
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.
Forward-Chaining Versus A Graph Approach As The Inference Engine In Expert Systems
NASA Astrophysics Data System (ADS)
Neapolitan, Richard E.
1986-03-01
Rule-based expert systems are those in which a certain number of IF-THEN rules are assumed to be true. Based on the verity of some assertions, the rules deduce as many new conclusions as possible. A standard technique used to make these deductions is forward-chaining. In forward-chaining, the program or 'inference engine' cycles through the rules. At each rule, the premises for the rule are checked against the current true assertions. If all the premises are found, the conclusion is added to the list of true assertions. At that point it is necessary to start over at the first rule, since the new conclusion may be a premise in a rule already checked. Therefore, each time a new conclusion is deduced it is necessary to start the rule checking procedure over. This process continues until no new conclusions are added and the end of the list of rules is reached. The above process, although quite costly in terms of CPU cycles due to the necessity of repeatedly starting the process over, is necessary if the rules contain 'pattern variables'. An example of such a rule is, 'IF X IS A BACTERIA, THEN X CAN BE TREATED WITH ANTIBIOTICS'. Since the rule can lead to conclusions for many values of X, it is necessary to check each premise in the rule against every true assertion producing an association list to be used in the checking of the next premise. However, if the rule does not contain variable data, as is the case in many current expert systems, then a rule can lead to only one conclusion. In this case, the rules can be stored in a graph, and the true assertions in an assertion list. The assertion list is traversed only once; at each assertion a premise is triggered in all the rules which have that assertion as a premise. When all premises for a rule trigger, the rule's conclusion is added to the END of the list of assertions. It must be added at the end so that it will eventually be used to make further deductions. In the current paper, the two methods are described in detail, the relative advantages of each is discussed, and a benchmark comparing the CPU cycles consumed by each is included. It is also shown that, in the case of reasoning under uncertainty, it is possible to properly combine the certainties derived from rules arguing for the same conclusion when the graph approach is used.
NASA Astrophysics Data System (ADS)
Kiso, Atsushi; Seki, Hirokazu
This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.
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
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
Type Inference Relevance to Telescoping Languages
Chauhan, Arun
Type Inference Relevance to Telescoping Languages Arun Chauhan COMP 612 December 6, 2002 #12;Questions Â· Why do we need type inference? Â· Can we leverage the type inference work in the programming languages community? COMP 612: Type Inference December 6, 2002 #12;Towards High-Level Systems for High
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.
San, Phyo Phyo; Ling, Sai Ho; Nguyen, Hung T
2012-01-01
Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. PMID:23367375
NASA Astrophysics Data System (ADS)
Christensen, Claire Petra
Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author'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
Ganesan, S; Victoire, T Aruldoss Albert; Vijayalakshmy, G
2014-01-01
In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal. PMID:24589837
Azarkhish, Iman; Raoufy, Mohammad Reza; Gharibzadeh, Shahriar
2012-06-01
Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision. PMID:21503744
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.
Sha, Edwin
based system. This paper presents algorithms to minimize the number of nodesin the graph using fuzzy operations. INTRODUCTION Fuzzy logic and approximate reasoning [11] are widely used in rulebased expert system on infer ences. This paper presents algorithms to automatically minimize a given fuzzy system so
NASA Technical Reports Server (NTRS)
Sigwarth, John B.; Bekerat, Hamed A.
2008-01-01
The Visible Imaging System (VIS) on the polar spacecraft provided time sequences of auroral images at multiple wavelengths that yield information of auroral dynamics on a global scale with a spatial resolution of - 20 km and temporal resolution of approx. 1 minute. Time sequences of VIS images in which the aurora was highly dynamic are used to infer global maps for the electron precipitation parameters, energy flux and characteristic energies, and ionospheric conductances. The maps are inferred from the corresponding VIS images using an auroral model (Lumerzheim et al., 1987). The temporal and spatial resolution of the VIS inferred patterns are unprecedented. The inferred patterns are highly structured and vary significantly on a time scale of less than 5 minutes. These patterns can be very beneficial for global physics-based numerical models for the high-latitude ionosphere which previously had to rely on statistical models for the electron precipitation and ionospheric conductance.
Hydrological connectivity inferred from diatom transport through the riparian-stream system
NASA Astrophysics Data System (ADS)
Martínez-Carreras, N.; Wetzel, C. E.; Frentress, J.; Ector, L.; McDonnell, J. J.; Hoffmann, L.; Pfister, L.
2015-07-01
Diatoms (Bacillariophyta) are one of the most common and diverse algal groups (ca. 200 000 species, ? 10-200 ?m, unicellular, eukaryotic). Here we investigate the potential of aerial diatoms (i.e. diatoms nearly exclusively occurring outside water bodies, in wet, moist or temporarily dry places) to infer surface hydrological connectivity between hillslope-riparian-stream (HRS) landscape units during storm runoff events. We present data from the Weierbach catchment (0.45 km2, northwestern Luxembourg) that quantify the relative abundance of aerial diatom species on hillslopes and in riparian zones (i.e. surface soils, litter, bryophytes and vegetation) and within streams (i.e. stream water, epilithon and epipelon). We tested the hypothesis that different diatom species assemblages inhabit specific moisture domains of the catchment (i.e. HRS units) and, consequently, the presence of certain species assemblages in the stream during runoff events offers the potential for recording whether there was hydrological connectivity between these domains or not. We found that a higher percentage of aerial diatom species was present in samples collected from the riparian and hillslope zones than inside the stream. However, diatoms were absent on hillslopes covered by dry litter and the quantities of diatoms (in absolute numbers) were small in the rest of hillslope samples. This limits their use for inferring hillslope-riparian zone connectivity. Our results also showed that aerial diatom abundance in the stream increased systematically during all sampled events (n = 11, 2011-2012) in response to incident precipitation and increasing discharge. This transport of aerial diatoms during events suggested a rapid connectivity between the soil surface and the stream. Diatom transport data were compared to two-component hydrograph separation, and end-member mixing analysis (EMMA) using stream water chemistry and stable isotope data. Hillslope overland flow was insignificant during most sampled events. This research suggests that diatoms were likely sourced exclusively from the riparian zone, since it was not only the largest aerial diatom reservoir, but also since soil water from the riparian zone was a major streamflow source during rainfall events under both wet and dry antecedent conditions. In comparison to other tracer methods, diatoms require taxonomy knowledge and a rather large processing time. However, they can provide unequivocal evidence of hydrological connectivity and potentially be used at larger catchment scales.
Novichkov, Pavel S.; Rodionov, Dmitry A.; Stavrovskaya, Elena D.; Novichkova, Elena S.; Kazakov, Alexey E.; Gelfand, Mikhail S.; Arkin, Adam P.; Mironov, Andrey A.; Dubchak, Inna
2010-05-26
RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.
3. Knowledge representation and inference
Keil, David M.
expert systems work D. Keil Artificial Intelligence 7/13 4 #12;3. Knowledge representation and inference3. Knowledge representation and inference D. Keil Artificial Intelligence 7/13 1D. Keil Artificial Intelligence 7/13 David Keil, Framingham State University CSCI 300 Artificial Intelligence 3. Knowledge
Sliding mode neural network inference fuzzy logic control for active suspension systems
Nizar Al-Holou; Tarek Lahdhiri; Dae Sung Joo; Jonathan Weaver; Faysal Al-Abbas
2002-01-01
In the automotive industry, suspension systems are designed to provide desirable vehicle ride and handling properties. This paper presents the development of a robust intelligent nonlinear controller for active suspension systems based on a comprehensive and realistic nonlinear model. The inherent complex nonlinear system model's structure, and the presence of parameter uncertainties, have increased the difficulties of applying conventional linear
Comparing Mathematical Models on the Problem of Network Inference
Zell, Andreas
Comparing Mathematical Models on the Problem of Network Inference Christian Spieth Centre. We focus on the evaluation of the performance of differ- ent mathematical models on the inference Evolutionary Computation, Inference, Systems Biology 1. INTRODUCTION Gene regulatory networks (GRNs) represent
NASA Astrophysics Data System (ADS)
Murray, J. R.; Svarc, J. L.; Pollitz, F. F.; Floyd, M.; Funning, G.; Johanson, I. A.
2014-12-01
Tectonic ground deformation due to the 24 August 2014 M6 South Napa earthquake was recorded by continuous GPS (CGPS) sites of the Plate Boundary Observatory, Bay Area Regional Deformation, and USGS networks. Additionally, survey-mode GPS (SGPS) measurements were carried out following the event to densify the spatial coverage and record postseismic deformation. We compare earthquake offsets estimated using two sets of time series for the same sites, one with position estimates at five minute intervals and the other at one day intervals. On average the offset magnitudes from the five-minute positions are ~70% those estimated from the daily data, demonstrating that substantial postseismic deformation occurred immediately following the coseismic slip. Fitting the daily position time series for sites within ~35 km of the epicenter with a combination of coseismic offset and a logarithmic decay that begins immediately following the event indicates that cumulative displacement from 25 August 2014 to 24 September 2014 is on average ~70% of the estimated displacement on 24 August at these sites. While earthquakes on creeping faults of the San Andreas system have often generated postseismic displacement of similar magnitude to the coseismic, the mapped trace associated with this earthquake was not known to creep. Using the coseismic offsets estimated from the five-minute solutions and a Bayesian inference approach, the most likely planar fault that passes through the epicenter and intersects the Earth's surface is vertical and strikes 155o, in good agreement with seismic moment tensor estimates. The peak GPS-inferred coseismic slip extends ~12 km northwest and up-dip of the hypocenter. Initial postseismic slip models derived from GPS data show shallow afterslip near and to the southeast of the inferred coseismic slip; the afterslip is generally shallower and southeast of the zone of aftershocks. However, the resulting GPS residuals exhibit more complex spatial patterns that are not well-fit with a simple planar geometry. Further collection and analysis of postseismic SGPS data, in combination with other geodetic observations, will help to characterize the postseismic deformation source process, its temporal evolution, and its relation to aftershocks.
[Occurence of salmonellae in laying hens in different housing systems and inferences for control].
Methner, Ulrich; Diller, Roland; Reiche, Renate; Böhland, Karin
2006-01-01
As eggs represent now as ever the most important source for Salmonella infection in human beings and because of the currently occurring shift in housing conditions for laying hens from conventional cages to alternative systems it was studied whether the Salmonella prevalence in layers is influenced by the housing system. Following systems were considered: organic farming with free range management systems, floor management systems with free range, floor management systems without free range, conventional cages. 453 pooled faecal samples as single or double examination per herd from 329 flocks in different housing systems for table egg production from three Federal Lander were examined bacteriologically. The share of layer flocks which were Salmonella positive at least once independently of the housing system amounted to 32.2%. Analysis of the Salmonella findings in the single housing systems revealed that the share of Salmonella positive flocks was higher in conventional cage systems (46.3%) than in alternative housing systems (32.996% in organic farming with free range management systems, 21.9% in floor management systems with free range, 23.4% in floor management systems without free range). The results of the study clearly show that Salmonella Enteritidis (mostly phage type 4, other phage types rarely) presents with a share of 78% the dominant serovar in laying hens. The total number of all other serovars (apart from Salmonella Enteritidis and subspecies I rough) reached a share of ca. 14%, however, no other single serovar was dominant. As Salmonella Enteritidis is the predominant serovar in laying hens it is strongly recommended to use Salmonella Enteritidis vaccines for immunisation programmes of chickens during the rearing period. Because of the high prevalence of Salmonella organisms in the different housing systems, detailed information on the epidemiology of Salmonella in laying hens are needed to introduce effective control measures. Of particular interest is the question whether the Salmonella findings in laying flocks are the result of multiplication of already existing Salmonella organisms in the animals or whether the bacteria are introduced only during the laying period. PMID:17172134
Fuzzy inference based edge detection system using Sobel and Laplacian of Gaussian operators
J. Patel; J. Patwardhan; K. Sankhe; R. Kumbhare
2011-01-01
This paper presents a new edge detection algorithm based on fuzzy system and fuzzy rules. Different approaches used before for detecting edges have some advantages and disadvantages like false edges are detected, some important edges are missed, noise around the corners etc. So, in order to reduce these types of effect special fuzzy system are used with inputs which are
Thrun, Sebastian
21 Inferring Networks of Diffusion and Influence MANUEL GOMEZ-RODRIGUEZ, Stanford University by developing a method for tracing paths of dif- fusion and influence through networks and inferring, blogs, news media, meme-tracking, social networks ACM Reference Format: Gomez-Rodriguez, M., Leskovec, J
Bayesian Inference for Reliability of Systems and Networks Using the Survival Signature.
Aslett, Louis J M; Coolen, Frank P A; Wilson, Simon P
2014-06-11
The concept of survival signature has recently been introduced as an alternative to the signature for reliability quantification of systems. While these two concepts are closely related for systems consisting of a single type of component, the survival signature is also suitable for systems with multiple types of component, which is not the case for the signature. This also enables the use of the survival signature for reliability of networks. In this article, we present the use of the survival signature for reliability quantification of systems and networks from a Bayesian perspective. We assume that data are available on tested components that are exchangeable with those in the actual system or network of interest. These data consist of failure times and possibly right-censoring times. We present both a nonparametric and parametric approach. PMID:24920552
Cho, Sung-Bae
Incident Inference System using Outdoor Environment Recognizer based on Probability Ju-Won HwangO Young]. . . , . . , . . , . . . 2. . Speed, Background Sound, Traffic Density, Lanes Blocked, Flow, Alarm . . , . , , . . . . 1 12 Detection Rate 100%, False alarm Rate 28%, 2 14 Detection Rate 82%, False alarm Rate 33
Magnetospheric current systems as inferred from SYM and ASY mid-latitude indices
NASA Astrophysics Data System (ADS)
Ganushkina, Natalia; Dubyagin, Stepan
2015-04-01
Separating the contributions from different current systems from point magnetic field measurements and interpreting them as belonging to one system or another is very difficult, and caution must be used when deciphering near-Earth currents from either data or modeling results. At the same time, there are other continuously measured quantities, which can provide, though indirectly, information about the dynamics of the magnetospheric current systems. The SYM-H and ASY-H indices, computed from the observations of magnetic field at low latitude ground-based stations, contain contributions from major magnetospheric current systems, such as the symmetric and asymmetric ring current, tail current, magnetopause currents and field-aligned currents. Highly distorted magnetospheric magnetic field during storm times due to disturbances in the current systems is reflected in the SYM-H and ASY-H observed variations. Using empirical magnetospheric models we study the relative contribution from different current systems to the SYM and ASY mid-latitude indices. It was found that the models can reproduce ground based mid-latitude indices rather well. The good agreement between the indices computed using magnetospheric models and real ones indicates that purely ionospheric current systems, on average, give modest contribution to these indices. The superposed epoch analysis of the indices computed using the models shows that the cross-tail current gives dominant contribution to SYM-H index during the main phase though this contribution can not be separated from FAC region 2 and partial ring current contributions since these systems are overlapped. The relative contribution from symmetric ring current to SYM-H starts to increase a bit prior or just after SYM-H minimum and attains its maximum during recovery phase. The ASY-H and ASY-D indices are controlled by interplay between three current systems which close via the ionosphere. The region 1 FAC gives the largest contribution to ASY-H and ASY-D indices during the main phase, though, region 2 FAC and partial ring current contributions are also prominent. The partial ring current is the main contributor to the ASY indices during the recovery phase. In addition, we discuss the application of these results to resolving the long-debated inconsistencies of the substorm-controlled geomagnetic storm scenario.
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...
Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code
Pratt, Vaughan
right, and any contradictory action is probably wrong without knowing the correct behavior. Our approach paired these calls by coincidence. If the pairing happens 999 out of 1000 times, though reduction in the manual e#11;ort needed to check a large system. Com- pared to our previous work [9
A neural network mode inference engine for the advisory system for training and safety
Nguyen, Thinh Xuan
1996-01-01
for different phases of the flight. In order to perform this task, the ASTRAS system is endowed with an artificial intelligence engine or Situation Recognizer (SR) which is able to discern the flight mode from sensor readings. The current SR is based on fuzzy...
Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H
2014-11-01
Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design. PMID:24953199
Machine Learning and Inference Laboratory The AQ19 System for Machine Learning
Michalski, Ryszard S.
Discovery: A General Description and User's Guide Ryzsard S. Michalski Kenneth A. Kaufman MLI 01-2 P 01-1 March, 2001 MLI 03-1- School of Computational Sciences George Mason University #12;THE AQ19 SYSTEM-4444 {michalski, kaufman}@mli.gmu.edu http://www.mli.gmu.edu Summary This report provides a description and a user
NASA Astrophysics Data System (ADS)
Winter, Steven John
Methodology development was conducted to incorporate a modular knowledge-base representation into an expert system engineering design application. The objective for using multidisciplinary methodologies in defining a design system was to develop a system framework that would be applicable to a wide range of engineering applications. The technique of "knowledge clustering" was used to construct a general decision tree for all factual information relating to the design application. This construction combined the design process surface knowledge and specific application depth knowledge. Utilization of both levels of knowledge created a system capable of processing multiple controlling tasks including; organizing factual information relative to the cognitive levels of the design process, building finite element models for depth knowledge analysis, developing a standardized finite element code for parallel processing, and determining a best solution generated by design optimization procedures. Proof of concept for the methodology developed here is shown in the implementation of an application defining the analysis and optimization of a composite aircraft canard subjected to a general compound loading condition. This application contained a wide range of factual information and heuristic rules. The analysis tools used included a finite element (FE) processor and numerical optimizer. An advisory knowledge-base was also developed to provide a standard for conversion of serial FE code for parallel processing. All knowledge-bases developed operated as either an advisory, selection, or classification systems. Laminate properties are limited to even-numbered, quasi-isotropic ply stacking sequences. This retained full influence of the coupled in-plane and bending effects of the structures behavior. The canard is modeled as a constant thickness plate and discretized into a varying number of four or nine-noded, quadrilateral, shear-deformable plate elements. The benefit gained by a designer from using this design methodology is presented by examining the capability of the system to satisfy the different levels of engineering design cognitive abilities. Numerical results of design iterations are provided to detail the expert system's advise in feasible region identification, and multiple iteration outcomes are used to justify solution assessment rules used in controlling the optimization process.
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 ...
Inferring population history from genealogies
Lohse, Konrad R.
2010-01-01
This thesis investigates a range of genealogical approaches to making quantitative inferences about the spatial and demographic history of populations with application to two insect systems: A local radiation of high ...
NASA Astrophysics Data System (ADS)
Bearden, Kathryn
Interpenetrating polymer networks (IPNs), where polymer chains mechanically entangle during network formation, are of interest for their unique properties. The reaction sequence of a DGEBF epoxy/polybutadiene-dimethacrylate simultaneous IPN system was varied with differing catalysts to observe the correlation between reaction steps and physical properties. When the acrylate components were reacted first an IPN with two glass transitions and discrete phase separation was observed via scanning electron microscopy (SEM). When all components were reacted in parallel, two glass transitions were also observed but the morphology presented a single phase or a visible macro phase seperation. The IPN showed an increase in fracture toughness but a decrease in tensile strength compared to the single phase system and an epoxy control. Varying the amounts of polybutadiene-dimethacrylate in relation to the epoxy also showed a limit to the toughening effect.
Immediate inferences from quantified assertions.
Khemlani, Sangeet; Lotstein, Max; Trafton, J Gregory; Johnson-Laird, P N
2015-10-01
We propose a theory of immediate inferences from assertions containing a single quantifier, such as: All of the artists are bakers; therefore, some of the bakers are artists. The theory is based on mental models and is implemented in a computer program, mReasoner. It predicts three main levels of increasing difficulty: (a) immediate inferences in which the premise and conclusion have identical meanings; (b) those in which the initial mental model of the premise yields the correct conclusion; and (c) those in which only an alternative to the initial model establishes the correct conclusion. These levels of difficulty were corroborated for inferences to necessary conclusions (in a reanalysis of data from Newstead, S. E., & Griggs, R. A. (1983). Drawing inferences from quantified statements: A study of the square of opposition. Journal of Verbal Learning and Verbal Behavior, 22, 535-546), for inferences to modal conclusions, such as, it is possible that all of the bakers are artists (Experiment 1), for inferences with unorthodox quantifiers, such as, most of the artists (Experiment 2), and for inferences about the consistency of pairs of quantified assertions (Experiment 3). The theory also includes three parameters in a stochastic system that predicted quantitative differences in accuracy within the three main sorts of inference. PMID:25607245
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
Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference
NASA Astrophysics Data System (ADS)
Worden, K.; Hensman, J. J.
2012-10-01
The aim of this paper is to provide an overview of the possible advantages of adopting a Bayesian approach to nonlinear system identification in structural dynamics. In contrast to identification schemes which estimate maximum likelihood values (or other point estimates) for parameters, the Bayesian scheme discussed here provides information about the complete probability density functions of parameter estimates without adopting restrictive assumptions about their nature. Among other advantages of the Bayesian viewpoint are the abilities to make informed decisions about model selection and also to effectively make predictions over entire classes of models, with each individual model weighted according to its ability to explain the observed data. The approach is illustrated using data from simulated systems, first a Duffing oscillator and then a new application to hysteretic system of the Bouc-Wen type. The modelling and identification of the latter type of system has long presented problems due to the fact that commonly used model structures like the Bouc-Wen model are nonlinear in the parameters, or have unmeasured states, etc. These issues have been dealt with in the past by adopting an optimisation-based approach to the problem; in particular, the differential evolution algorithm has proved very effective. An objective of the current paper is to illustrate how the Bayesian approach provides the same information and more as the optimisation approach; it yields parameter estimates and their associated confidence intervals, but can also provide confidence bounds on model predictions and evidence measures which can be used to select the most appropriate model from a candidate set. A new model selection criterion in this context - the Deviance Information Criterion (DIC) - is presented here.
Methane leakage from evolving petroleum systems: Masses, rates and inferences for climate feedback
NASA Astrophysics Data System (ADS)
Berbesi, L. A.; di Primio, R.; Anka, Z.; Horsfield, B.; Wilkes, H.
2014-02-01
The immense mass of organic carbon contained in sedimentary systems, currently estimated at 1.56×1010 Tg (Des Marais et al., 1992), bears the potential of affecting global climate through the release of thermally or biologically generated methane to the atmosphere. Here we investigate the potential of naturally-occurring gas leakage, controlled by petroleum generation and degradation as a forcing mechanism for climate at geologic time scales. We addressed the potential methane contributions to the atmosphere during the evolution of petroleum systems in two different, petroliferous geological settings: the Western Canada Sedimentary Basin (WCSB) and the Central Graben area of the North Sea. Besides 3D numerical simulation, different types of mass balance and theoretical approaches were applied depending on the data available and the processes taking place in each basin. In the case of the WCSB, we estimate maximum thermogenic methane leakage rates in the order of 10-2-10-3 Tg/yr, and maximum biogenic methane generation rates of 10-2 Tg/yr. In the case of the Central Graben, maximum estimates for thermogenic methane leakage are in the order in 10-3 Tg/yr. Extrapolation of our results to a global scale suggests that, at least as a single process, thermal gas generation in hydrocarbon kitchen areas would not be able to influence climate, although it may contribute to a positive feedback. Conversely, only the sudden release of subsurface methane accumulations, formed over geologic timescales, can possibly allow for petroleum systems to exert an effect on climate.
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
NASA Astrophysics Data System (ADS)
Abouelmagd, A.; McCabe, M. F.; Castro, M. C.; Sultan, M.; Jana, R. B.; Al-Mashharawi, S.
2014-12-01
One of the most valuable groundwater reserves in Saudi Arabia is the Saq aquifer system (SAS), a thick (400?1200 meters) sandstone unit that extends across 300,000 km2 in Saudi Arabia and neighboring Jordan. Due to its high productivity and high water quality, current pumping and overexploitation of the aquifer has significantly lowered the groundwater level over the years. Understanding the recharge regimes of the SAS is critical for the development of sustainable exploitation of water resources in the region and for the establishment of appropriate management practices. In this study, we investigate the hydrologic setting of the SAS and seek to differentiate the degree of paleo versus modern contributions using a range of geochemical approaches. Multiple groundwater samples were collected from deep production wells tapping the SAS at depths between 375?1800 m and across a range of locations. Samples were analyzed for their chemical concentrations, stable isotopic compositions (?18O and ?2H), and dissolved noble gas concentrations and isotopic ratios. Examining these data identifies unmixed pools of fossil groundwater at deeper depths as well as mixed shallower systems that indicate contributions from modern precipitation. Through isotopic and noble gas analyses, the relative age and timing of these recharge events was examined and show contributions from both glacial and inter-glacial periods, with some modest contributions from modern meteoric sources.
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.
Li, Xin; Li, Jing
2010-01-01
We study the haplotype inference problem from pedigree data under the zero recombination assumption, which is well supported by real data for tightly linked markers (i.e., single nucleotide polymorphisms (SNPs)) over a relatively large chromosome segment. We solve the problem in a rigorous mathematical manner by formulating genotype constraints as a linear system of inheritance variables. We then utilize disjoint-set structures to encode connectivity information among individuals, to detect constraints from genotypes, and to check consistency of constraints. On a tree pedigree without missing data, our algorithm can output a general solution as well as the number of total specific solutions in a nearly linear time O(mn · ?(n)), where m is the number of loci, n is the number of individuals and ? is the inverse Ackermann function4, which is a further improvement over existing ones3, 8, 12, 15. We also extend the idea to looped pedigrees and pedigrees with missing data by considering existing (partial) constraints on inheritance variables. The algorithm has been implemented in C++ and will be incorporated into our PedPhase package8. Experimental results show that it can correctly identify all 0-recombinant solutions with great efficiency. Comparisons with other two popular algorithms show that the proposed algorithm achieves 10 to 105-fold improvements over a variety of parameter settings. The experimental study also provides empirical evidences on the complexity bounds suggested by theoretical analysis. PMID:19642289
Davies, Andrew J; Hope, Max J
2015-07-15
Contingency plans are essential in guiding the response to marine oil spills. However, they are written before the pollution event occurs so must contain some degree of assumption and prediction and hence may be unsuitable for a real incident when it occurs. The use of Bayesian networks in ecology, environmental management, oil spill contingency planning and post-incident analysis is reviewed and analysed to establish their suitability for use as real-time environmental decision support systems during an oil spill response. It is demonstrated that Bayesian networks are appropriate for facilitating the re-assessment and re-validation of contingency plans following pollutant release, thus helping ensure that the optimum response strategy is adopted. This can minimise the possibility of sub-optimal response strategies causing additional environmental and socioeconomic damage beyond the original pollution event. PMID:26006775
NASA Technical Reports Server (NTRS)
Harrison, P. Ann
1993-01-01
All the NASA VEGetation Workbench (VEG) goals except the Learning System provide the scientist with several different techniques. When VEG is run, rules assist the scientist in selecting the best of the available techniques to apply to the sample of cover type data being studied. The techniques are stored in the VEG knowledge base. The design and implementation of an interface that allows the scientist to add new techniques to VEG without assistance from the developer were completed. A new interface that enables the scientist to add techniques to VEG without assistance from the developer was designed and implemented. This interface does not require the scientist to have a thorough knowledge of Knowledge Engineering Environment (KEE) by Intellicorp or a detailed knowledge of the structure of VEG. The interface prompts the scientist to enter the required information about the new technique. It prompts the scientist to enter the required Common Lisp functions for executing the technique and the left hand side of the rule that causes the technique to be selected. A template for each function and rule and detailed instructions about the arguments of the functions, the values they should return, and the format of the rule are displayed. Checks are made to ensure that the required data were entered, the functions compiled correctly, and the rule parsed correctly before the new technique is stored. The additional techniques are stored separately from the VEG knowledge base. When the VEG knowledge base is loaded, the additional techniques are not normally loaded. The interface allows the scientist the option of adding all the previously defined new techniques before running VEG. When the techniques are added, the required units to store the additional techniques are created automatically in the correct places in the VEG knowledge base. The methods file containing the functions required by the additional techniques is loaded. New rule units are created to store the new rules. The interface that allow the scientist to select which techniques to use is updated automatically to include the new techniques. Task H was completed. The interface that allows the scientist to add techniques to VEG was implemented and comprehensively tested. The Common Lisp code for the Add Techniques system is listed in Appendix A.
Early accretion of protoplanets inferred from a reduced inner solar system 26Al inventory
NASA Astrophysics Data System (ADS)
Schiller, Martin; Connelly, James N.; Glad, Aslaug C.; Mikouchi, Takashi; Bizzarro, Martin
2015-06-01
The mechanisms and timescales of accretion of 10-1000 km sized planetesimals, the building blocks of planets, are not yet well understood. With planetesimal melting predominantly driven by the decay of the short-lived radionuclide 26Al (26Al?26Mg; t1/2 = 0.73 Ma), its initial abundance determines the permissible timeframe of planetesimal-scale melting and its subsequent cooling history. Currently, precise knowledge about the initial 26Al abundance [(26Al/27Al)0] exists only for the oldest known solids, calcium aluminum-rich inclusions (CAIs) - the so-called canonical value. We have determined the 26Al/27Al of three angrite meteorites, D'Orbigny, Sahara 99555 and NWA 1670, at their time of crystallization, which corresponds to (3.98 ± 0.15) ×10-7, (3.64 ± 0.18) ×10-7, and (5.92 ± 0.59) ×10-7, respectively. Combined with a newly determined absolute U-corrected Pb-Pb age for NWA 1670 of 4564.39 ± 0.24 Ma and published U-corrected Pb-Pb ages for the other two angrites, this allows us to calculate an initial (26Al/27Al)0 of (1.33-0.18+0.21) ×10-5 for the angrite parent body (APB) precursor material at the time of CAI formation, a value four times lower than the accepted canonical value of 5.25 ×10-5. Based on their similar 54Cr/52Cr ratios, most inner solar system materials likely accreted from material containing a similar 26Al/27Al ratio as the APB precursor at the time of CAI formation. To satisfy the abundant evidence for widespread planetesimal differentiation, the subcanonical 26Al budget requires that differentiated planetesimals, and hence protoplanets, accreted rapidly within 0.25 ± 0.15 Ma of the formation of canonical CAIs.
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.
Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien
2013-01-01
Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023
Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor
NASA Astrophysics Data System (ADS)
Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.
2011-08-01
Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.
Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems
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
Surya Pemmaraju; Sunanda Mitral; Yao-yang Shieh; Glenn H. Roberson
1995-01-01
Segmentation of medical images is a challenging problem in the field of image analysis. Several diagnostics are based on proper segmentation of the digitized image. Segmentation of medical images is needed for applications involving estimation of the boundary of an object, classification of tissue abnormalities, shape analysis, contour detection and texture segmentation. Despite the existence of several techniques, segmentation of
Towards intelligent self-care: Multisensor monitoring and neuro-fuzzy behavior modelling
E. Chan; D. Wang; M. Pasquier
2008-01-01
The work reported in this paper investigates the problem of intelligent patient monitoring, whereby a combination of smart sensor technology and computational intelligence techniques is used to observe then understand the behavior of a person in a self-care context, such as a patient in his hospital room or an elderly in her home. The key objective is to automatically derive
Solving constraint satisfaction and optimization problems by a neuro-fuzzy approach.
Cavalieri, S; Russo, M
1999-01-01
The solution of constrained satisfaction and constrained optimization problems using a Hopfield model requires determination of the values of a certain number of coefficients linked to the surrounding conditions of the problem. It is quite difficult to determine these values, mainly because a heuristic search is necessary. This is not only time-consuming but may lead to solutions that are far from optimal, or even nonvalid ones. So far, there have been no works in literature offering a general method for the search for coefficents with will guarantee optimal or close to optimal solutions. This paper proposes a fuzzy approach which allows automatic determination of Hopfield coefficients. PMID:18252367
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.
A neuro-fuzzy controller for mobile robot navigation and multirobot convoying.
Ng, K C; Trivedi, M M
1998-01-01
A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets. PMID:18256001
User/Tutor Optimal Learning Path in E-Learning Using Comprehensive Neuro-Fuzzy Approach
ERIC Educational Resources Information Center
Fazlollahtabar, Hamed; Mahdavi, Iraj
2009-01-01
Internet evolution has affected all industrial, commercial, and especially learning activities in the new context of e-learning. Due to cost, time, or flexibility e-learning has been adopted by participators as an alternative training method. By development of computer-based devices and new methods of teaching, e-learning has emerged. The…
A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller
NASA Astrophysics Data System (ADS)
Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi
This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.
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
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.
A Dynamic User Posture Inference Scheme for Mobile Devices
A dynamic user context inference method is one of the important technologies for realizing context-aware services. In this paper, we show a context inference scheme that realizes a user posture inference with only one acceleration sensor embedded in a mobile handset. To improve inference accuracy, the system automatically detects the sensor position on the user's body and dynamically selects the
Boutalis, Yiannis; Theodoridis, Dimitris C; Christodoulou, Manolis A
2009-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation. PMID:19273046
Immune System Modeling with Infer.NET Vincent Y. F. Tan, John Winn, Angela Simpson, Adnan Custovic
Winn, John
the Manchester Asthma and Allergy Study (MAAS). The analysis is achieved using the Infer.NET 1 tool which allows classes, the children who are vulnerable to allergies and have a high probability of having asthma (22%) are identified. For 5 sensitization classes, children in the first cluster, those who are vulnerable to allergies
Tolga Onel; Cem Ersoy; Erdal Cayirci
2002-01-01
A novel handoff decision algorithm, namely multicriteria handoff decision algorithm (MDA), for the virtual cell layout based mobile subsystem of the next generation tactical communications systems is introduced. In this algorithm, handoff decision metrics are received signal strength measurements from the access points, soft capacities of the access points, and relative directions and speeds of the access points and the
NASA Astrophysics Data System (ADS)
Moron, Vincent; Barbero, Renaud; Robertson, Andrew W.
2015-07-01
Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill score. In general, ANFIS models show superior results in terms of correlation coefficient for the overall case study. As a pioneer study, it is proposed that ANFIS is a promising tool for the purpose of seasonal predictions in Australia as they produce comparable accuracy using minimal inputs, require less development time and they are less complex compared to dynamic models.
Type Inference with Bounded Quantification
Sequeira, Dilip
In this thesis we study some of the problems which occur when type inference is used in a type system with subtyping. An underlying poset of atomic types is used as a basis for our subtyping systems. We argue that the class of Helly posets...
Dimension Inference in Spreadsheets Chris Chambers
Erwig, Martin
Dimension Inference in Spreadsheets Chris Chambers Oregon State University chambech@eecs.oregonstate.edu Martin Erwig Oregon State University erwig@eecs.oregonstate.edu Abstract We present a reasoning system for inferring dimension information in spreadsheets. This system can be used to check the consistency
NASA Astrophysics Data System (ADS)
Ozbulut, Osman E.; Hurlebaus, Stefan
2009-03-01
This paper proposes a neuro-fuzzy model of NiTi shape memory alloy (SMA) wires that is capable of capturing behavior of superelastic SMAs at different temperatures and at various loading rates while remaining simple enough to realize numerical simulations. First, in order to collect data, uniaxial tensile tests are conducted on superelastic wires in the temperature range of 0 ÂºC to 40 ÂºC, and at the loading frequencies of 0.05 Hz to 2 Hz that is the range of interest for seismic applications. Then, an adaptive neuro-fuzzy inference system (ANFIS) is employed to construct a model of SMAs based on experimental input-output data pairs. The fuzzy model obtained from ANFIS training is validated by using an experimental data set that is not used during training. Upon having a model that can represent behavior of superelastic SMAs at various ambient temperature and loading-rates, nonlinear simulation of a multi-span continuous bridge isolated by rubber bearings that is equipped with SMA dampers is carried out. Response of the bridge to a historical earthquake record is presented at different ambient temperatures in order to evaluate the effect of temperature on the performance of the structure. It is shown that SMA damping elements can effectively decrease peak deck displacement and the relative displacement between piers and superstructure in an isolated bridge while recovering all the deformations to their original position.
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…
Kelly, Tim
2007-01-01
Elsevier Ltd. All rights reserved. Keywords: Safety; Critical; Artificial neural network; Neuro-fuzzy defines a type of constrained artificial neural network (ANN) that enables analytical certification with the FSOM. The constrained FSOM has been termed a `safety critical artificial neural network' (SCANN
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.
Inferring biotic interactions from proxies.
Morales-Castilla, Ignacio; Matias, Miguel G; Gravel, Dominique; Araújo, Miguel B
2015-06-01
Inferring biotic interactions from functional, phylogenetic and geographical proxies remains one great challenge in ecology. We propose a conceptual framework to infer the backbone of biotic interaction networks within regional species pools. First, interacting groups are identified to order links and remove forbidden interactions between species. Second, additional links are removed by examination of the geographical context in which species co-occur. Third, hypotheses are proposed to establish interaction probabilities between species. We illustrate the framework using published food-webs in terrestrial and marine systems. We conclude that preliminary descriptions of the web of life can be made by careful integration of data with theory. PMID:25922148
Eight challenges in phylodynamic inference.
Frost, Simon D W; Pybus, Oliver G; Gog, Julia R; Viboud, Cecile; Bonhoeffer, Sebastian; Bedford, Trevor
2015-03-01
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
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.…
Inference of Signal Transduction Networks from Double Causal Evidence
Sontag, Eduardo
inference, Signal transduction, Systems biology, Double causal evidence Most biological characteristics239 Chapter 16 Inference of Signal Transduction Networks from Double Causal Evidence Réka Albert software, to synthesize signal transduction networks from single and double causal evidences
Fuzzy Economizer control using a Prolog-C inference engine
Belur, Raghuveer R.
1993-01-01
This research is in two parts: I. Develop a generic tool to perform fuzzy inference on a wide class of systems.Thisis done using Prolog and C. 2.Develop a hierarchical control scheme using this fuzzy inference mechanism ...
St Onge, K R; Palmé, A E; Wright, S I; Lascoux, M
2012-07-01
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
Friston, Karl
2014-01-01
I greatly enjoyed Seth's compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)-which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue). These perspectives include perception as hypothesis testing, affordance, and sensorimotor contingencies. This commentary briefly rehearses the fundaments of active inference and offers a formal basis for Seth's key argument. PMID:24702520
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 ...
Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John
2014-01-01
Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long?term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short?range alignment and trilateration arrays and 48 near?fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half?space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M?5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic?hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.
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.
A Comparative Study of Soft Computing Methodologies in Identification of Robotic Manipulators
Efe, Mehmet Önder
-computing approaches. As the identification methods, Feedforward Neural Network ar- chitecture (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference-computing methodologies are the neural networks and fuzzy logic. Neural networks provide the mathematical power
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
From Deep Inference to Proof Nets via Cut Elimination
Strassburger, Lutz
From Deep Inference to Proof Nets via Cut Elimination Lutz Straßburger INRIA Saclay in the deep inference system SKS for classical propositional logic can be translated into proof nets. Since the development of the two concepts of deep inference [Gug07] and proof nets [Gir87] the quest for the identity
Shi, Qinfeng "Javen"
Heuristics Learn graphs Infer graphs Lecture 10: PGM -- Structure Estimation Qinfeng (Javen) Shi 13 -- Structure Estimation #12;Heuristics Learn graphs Infer graphs Table of Contents I 1 Heuristics Manually specify a fixed graph Use a simple rule 2 Learn graphs Learn graphs from labels only Learn graphs from
Inductive Inference and Unsolvability
Leonard M. Adleman; Manuel Blum
1991-01-01
It is shown that many different problems have the same degree of unsolvability. Among these problems are: THE INDUCTIVE INFERENCE PROBLEM. Infer in the limit an index for a recursive function $f$ presented as $f(0), f(1), f(2),\\\\ldots$. THE RECURSIVE INDEX PROBLEM. Decide in the limit if $i$ is the index of a total recursive function. THE ZERO NONVARIANT PROBLEM. Decide
David H. Wolpert
2008-01-01
We show that physical devices that perform observation, prediction, or recollection share an underlying mathematical structure. We call devices with that structure “inference devices”. We present a set of existence and impossibility results concerning inference devices. These results hold independent of the precise physical laws governing our universe. In a limited sense, the impossibility results establish that Laplace was wrong
Rashad Sharaf; Mahmoud Reda Taha; Mohammed Tarbouchi; Aboelmagd Noureldin
2007-01-01
Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation\\u000a system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages\\u000a and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However,\\u000a KF-based integration techniques suffer from several limitations
Inferring signalling networks from images.
Evans, L; Sailem, H; Vargas, P Pascual; Bakal, C
2013-10-01
The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens. PMID:23841886
Azamathulla, H Md; Ab Ghani, Aminuddin; Fei, Seow Yen
2012-03-01
The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r(2) = 0.98 and RMSE = 0.002431) compared to the existing predictor. PMID:22389640
A Semi-automatic System to Infer Complex 3-D Shapes from Photographs Qian Chen and Grard Medioni
Southern California, University of
, and use this information to drive models at the other end of the network. In such a system, it is critical the foreground and the background, thus easy to be manually removed. To make the system more practical, we insist the points' coordinates and the cameras' poses are estimated using least square pose estimation. This removes
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
A wavelet transform based feature extraction and classification of cardiac disorder.
Sumathi, S; Beaulah, H Lilly; Vanithamani, R
2014-09-01
This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652
Anfis Approach for Sssc Controller Design for the Improvement of Transient Stability Performance
NASA Astrophysics Data System (ADS)
Khuntia, Swasti R.; Panda, Sidhartha
2011-06-01
In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Static Synchronous Series Compensator (SSSC)-based controller for improvement of transient stability. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controller of SSSC. It is observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances. The results prove that the proposed SSSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for SSSC.
ANFIS based modeling and inverse control of a thin SMA wire
NASA Astrophysics Data System (ADS)
Kilicarslan, Atilla; Song, Gangbing; Grigoriadis, Karolos
2008-03-01
In this work, we propose an Adaptive Neuro Fuzzy Inference System (ANFIS) based hysteresis modeling and control strategy for a thin Shape Memory Alloy (SMA) wire. Controlling the SMA wire is a challenging problem because of its dynamic hysteretic behavior. By using a hybrid learning procedure ANFIS architectures are powerful tools for many applications, such as identifying nonlinear parameters in a controlled system, predicting chaotic time series and modeling nonlinear functions. We tested our ANFIS model by making it predict major and minor hysteresis loops in different driving frequencies and compared them with the experimental data. To compensate the hysteretic effect, we used an inverse ANFIS model and used it directly as a controller. After dramatically reducing the hysteretic effect, we implemented a PI control to fine tune the response.
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.
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. ...
Quantum inferring acausal structure
Adam Glos; Dariusz Kurzyk
2015-05-27
This paper presents a generalization of probability theory and inferring structures in a field of quantum information theory. First, the formalism of quantum information theory is introduced. Next, quantum theory is used to define a quantum conditional operators which are fundamental in describing quantum inferring structures. The result of reasoning performance over the structures was presented in a Monty Hall game. The experiment shows that entanglement of quantum states has influence on optimal behaviour of player in the game.
Social Inference Through Technology
NASA Astrophysics Data System (ADS)
Oulasvirta, Antti
Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.
Koonin, Eugene V
2006-01-01
Background The core enzymes of the DNA replication systems show striking diversity among cellular life forms and more so among viruses. In particular, and counter-intuitively, given the central role of DNA in all cells and the mechanistic uniformity of replication, the core enzymes of the replication systems of bacteria and archaea (as well as eukaryotes) are unrelated or extremely distantly related. Viruses and plasmids, in addition, possess at least two unique DNA replication systems, namely, the protein-primed and rolling circle modalities of replication. This unexpected diversity makes the origin and evolution of DNA replication systems a particularly challenging and intriguing problem in evolutionary biology. Results I propose a specific succession for the emergence of different DNA replication systems, drawing argument from the differences in their representation among viruses and other selfish replicating elements. In a striking pattern, the DNA replication systems of viruses infecting bacteria and eukaryotes are dominated by the archaeal-type B-family DNA polymerase (PolB) whereas the bacterial replicative DNA polymerase (PolC) is present only in a handful of bacteriophage genomes. There is no apparent mechanistic impediment to the involvement of the bacterial-type replication machinery in viral DNA replication. Therefore, I hypothesize that the observed, markedly unequal distribution of the replicative DNA polymerases among the known cellular and viral replication systems has a historical explanation. I propose that, among the two types of DNA replication machineries that are found in extant life forms, the archaeal-type, PolB-based system evolved first and had already given rise to a variety of diverse viruses and other selfish elements before the advent of the bacterial, PolC-based machinery. Conceivably, at that stage of evolution, the niches for DNA-viral reproduction have been already filled with viruses replicating with the help of the archaeal 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
On the criticality of inferred models
NASA Astrophysics Data System (ADS)
Mastromatteo, Iacopo; Marsili, Matteo
2011-10-01
Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.
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...
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.
Owladeghaffari, H; Saeedi, G H R
2008-01-01
Approximately more than 90% of all coal production in Iranian underground mines is derived directly longwall mining method. Out of seam dilution is one of the essential problems in these mines. Therefore the dilution can impose the additional cost of mining and milling. As a result, recognition of the effective parameters on the dilution has a remarkable role in industry. In this way, this paper has analyzed the influence of 13 parameters (attributed variables) versus the decision attribute (dilution value), so that using two approximate reasoning methods, namely Rough Set Theory (RST) and Self Organizing Neuro- Fuzzy Inference System (SONFIS) the best rules on our collected data sets has been extracted. The other benefit of later methods is to predict new unknown cases. So, the reduced sets (reducts) by RST have been obtained. Therefore the emerged results by utilizing mentioned methods shows that the high sensitive variables are thickness of layer, length of stope, rate of advance, number of miners, type of...
NASA Astrophysics Data System (ADS)
Saeed, R. A.; Galybin, A. N.; Popov, V.
2013-01-01
This paper discusses condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.
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.
Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models
NASA Astrophysics Data System (ADS)
Mandal, Sukomal; Rao, Subba; N., Harish; Lokesha
2012-06-01
The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Shiri, Jalal
2012-06-01
Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg-Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others.
ANFIS-based approach for the estimation of transverse mixing coefficient.
Ahmad, Z; Azamathulla, H Md; Zakaria, N A
2011-01-01
Understanding of the fate of pollutants, disposed of in streams, is a matter of concern in recent years for the effective control of pollution. Transverse mixing of the pollutants in open channels is arguably more important than the longitudinal mixing and near-field mixing. Several attempts have been made to establish the relationship between the transverse mixing coefficient and bulk channel and flow parameters such as width, depth, shear velocity, friction factor, curvature and sinuosity. This paper presents adaptive neuro fuzzy inference system (ANFIS) approach to predict the transverse mixing coefficient in open channel flows. Available laboratory and field data for the transverse mixing coefficients covering wide range of channel and flow conditions are used for the development and testing of the proposed method. The proposed ANFIS approach produces satisfactory results (R(2)=0.945) compared to the artificial neural network (ANN) model and existing predictors for mixing coefficient. PMID:21411952
Rock mechanics modeling based on soft granulation theory
Owladeghaffari, H
2008-01-01
This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experiences (especially in fuzzy shape-incomplete information or superfluous), or engineering judgments, in each step of designing procedure, while the suitable instruments modeling are employed. In this manner and to extension of soft modeling instruments, using three combinations of Self Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp and fuzzy granules, from monitored data sets are obtained. The main underlined core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and sub fuzzy granules, within non fuzzy information (initial granulation) upon the open-close iterations. Using different criteria on balancing best granules (information pock...
The CO2 system in the Mediterranean Sea inferred from a 3D coupled physical-biogeochemical model
NASA Astrophysics Data System (ADS)
Ulses, Caroline; Kessouri, Fayçal; Estournel, Claude; Marsaleix, Patrick; Beuvier, Jonathan; Somot, Samuel; Touratier, Frank; Goyet, Catherine; Coppola, Laurent; Diamond, Emilie; Metzl, Nicolas
2015-04-01
The semi-enclosed Mediterranean Sea characterized by short residence times is considered as a region particularly sensitive to natural and anthropogenic forcing. Due to scarce CO2 measurements in the whole basin, the CO2 system, for instance the air-sea CO2 exchanges and the effects of the increase of atmospheric CO2, are poorly characterized. 3D physical-biogeochemical coupled models are unique tools that can provide integrated view and gain understanding in the temporal and spatial variation of the CO2 system variables (dissolved inorganic carbon, total alkalinity, partial pressure of CO2 and pH). An extended version of the biogeochemical model Eco3m-S (Auger et al., 2014), that describes the cycles of carbon, nitrogen, phosphorus and silica, was forced by a regional circulation model (Beuvier et al., 2012) to investigate the CO2 system in the Mediterranean Sea over a 13-years period (2001-2013). First, the quality of the modelling was evaluated through comparisons with satellite and in situ observations collected in the whole basin over the study period (Touratier and Goyet, 2009; 2011 ; Rivaro et al., 2010 ; Pujo-Pay et al., 2011 ; Alvarez et al, 2014). The model reasonably reproduced the various biological regimes (north-western phytoplanctonic bloom regime, oligotrophic eastern regime, etc.) as well as the recorded spatial distribution and temporal variations of the carbonate system variables. The coupled model was then used to estimate the air-sea pCO2 exchanges and the transport of DIC and TA towards the Atlantic Ocean at the Strait of Gibraltar.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Nimalsiri, Thusitha Bandara; Suriyaarachchi, Nuwan Buddhika; Hobbs, Bruce; Manzella, Adele; Fonseka, Morrel; Dharmagunawardena, H. A.; Subasinghe, Nalaka Deepal
2015-06-01
First comprehensive geothermal exploration in Sri Lanka was conducted in 2010 encompassing seven thermal springs, of which Kapurella records the highest temperature. The study consisted of passive magnetotelluric (MT) soundings, in which static shifts were corrected using time domain electromagnetic method (TDEM). A frequency range of 12,500-0.001 Hz was used for MT acquisition and polar diagrams were employed for dimensionality determination. MT and TDEM data were jointly inverted and 2D models were created using both transverse electric and transverse magnetic modes. A conductive southeast dipping structure is revealed from both phase pseudosections and the preferred 2D inversion model. A conductive formation starting at a depth of 7.5 km shows a direct link with the dipping structure. We suggest that these conductive structures are accounted for deep circulation and accumulation of groundwater. Our results show the geothermal reservoir of Kapurella system with a lateral extension of around 2.5 km and a depth range of 3 km. It is further found that the associated dolerite dike is not the source of heat although it could be acting as an impermeable barrier to form the reservoir. The results have indicated the location of the deep reservoir and the possible fluid path of the Kapurella system, which could be utilized to direct future geothermal studies. This pioneering study makes suggestions to improve future MT data acquisition and to use boreholes and other geophysical methods to improve the investigation of structures at depth.
NASA Astrophysics Data System (ADS)
Partsinevelos, Panagiotis; Kallimani, Christina; Tripolitsiotis, Achilleas
2015-06-01
Rockfall incidents affect civil security and hamper the sustainable growth of hard to access mountainous areas due to casualties, injuries and infrastructure loss. Rockfall occurrences cannot be easily prevented, whereas previous studies for rockfall multiple sensor early detection systems have focused on large scale incidents. However, even a single rock may cause the loss of a human life along transportation routes thus, it is highly important to establish methods for the early detection of small-scale rockfall incidents. Terrestrial photogrammetric techniques are prone to a series of errors leading to false alarm incidents, including vegetation, wind, and non relevant change in the scene under consideration. In this study, photogrammetric monitoring of rockfall prone slopes is established and the resulting multi-temporal change imagery is processed in order to minimize false alarm incidents. Integration of remote sensing imagery analysis techniques is hereby applied to enhance early detection of a rockfall. Experimental data demonstrated that an operational system able to identify a 10-cm rock movement within a 10% false alarm rate is technically feasible.
NASA Astrophysics Data System (ADS)
Takei, Yasuko
2001-01-01
Microstructural changes of partially molten media under deviatoric stress were investigated in a newly developed apparatus by deforming a large sample (a 70-mm cube) under a uniform pure shear stress. Borneol + melt system having a moderate dihedral angle and texturally equilibrated under hydrostatic stress was used as a partially molten rock analogue. The applied stress was small enough not to involve cataclastic-plastic deformation of the solid grains. Shear strain rate was about 10-8 s-1, and a stress exponent indicative of diffusion creep was obtained. During the deformation, sample microstructure was observed in situ by means of ultrasonic shear waves. The development of stress-induced anisotropy was successfully detected by shear wave splitting. The results obtained indicate that grain boundary contiguity in the direction of the least compressive stress (?3) was reduced with respect to the equilibrium texture and also that the relative values of liquid pressure and ?3 play an essential role for development of anisotropy. The developed anisotropy persisted as long as deviatoric stress was applied, but the initial isotropic structure was recovered by releasing this stress. Several interesting phenomena were involved in the structural change; these include shear creep-induced dilatancy, strong dependence of the timescale of structural recovery on the amount of deformation (memory effect), and relaxation creep after releasing stress. Scaling considerations using the Griffith theory shows that the structural changes observed in the present experimental system are expected to occur in the Earth as well.
Wu, Wei; Mast, Thomas G; Ziembko, Christopher; Breza, Joseph M; Contreras, Robert J
2013-01-01
We analyzed the spike discharge patterns of two types of neurons in the rodent peripheral gustatory system, Na specialists (NS) and acid generalists (AG) to lingual stimulation with NaCl, acetic acid, and mixtures of the two stimuli. Previous computational investigations found that both spike rate and spike timing contribute to taste quality coding. These studies used commonly accepted computational methods, but they do not provide a consistent statistical evaluation of spike trains. In this paper, we adopted a new computational framework that treated each spike train as an individual data point for computing summary statistics such as mean and variance in the spike train space. We found that these statistical summaries properly characterized the firing patterns (e. g. template and variability) and quantified the differences between NS and AG neurons. The same framework was also used to assess the discrimination performance of NS and AG neurons and to remove spontaneous background activity or "noise" from the spike train responses. The results indicated that the new metric system provided the desired decoding performance and noise-removal improved stimulus classification accuracy, especially of neurons with high spontaneous rates. In summary, this new method naturally conducts statistical analysis and neural decoding under one consistent framework, and the results demonstrated that individual peripheral-gustatory neurons generate a unique and reliable firing pattern during sensory stimulation and that this pattern can be reliably decoded. PMID:23738016
"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
Autonomous agricultural remote sensing systems with high spatial and temporal resolutions
NASA Astrophysics Data System (ADS)
Xiang, Haitao
In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield consistently for both seasons. The VT-R4 stages are the best period of time to estimate the corn yield. The SICMS system is only suitable for the RS research at a fixed location. In order to provide more flexibility of the RS image collection, a novel UAV based system has been studied. The UAV based agricultural RS system used a light helicopter platform equipped with a multi-spectral camera. The UAV control system consisted of an on-board and a ground station subsystem. For the on-board subsystem, an Extended Kalman Filter (EKF) based UAV navigation system was designed and implemented. The navigation system, using low cost inertial sensors, magnetometer, GPS and a single board computer, was capable of providing continuous estimates of UAV position and attitude at 50 Hz using sensor fusion techniques. The ground station subsystem was designed to be an interface between a human operator and the UAV to implement mission planning, flight command activation, and real-time flight monitoring. The navigation system is controlled by the ground station, and able to navigate the UAV in the air to reach the predefined waypoints and trigger the multi-spectral camera. By so doing, the aerial images at each point could be captured automatically. The developed UAV RS system can provide a maximum flexibility in crop field RS image collection. It is essential to perform the geometric correction and the geocoding before an aerial image can be used for precision farming. An automatic (no Ground Control Point (GCP) needed) UAV image georeferencing algorithm was developed. This algorithm can do the automatic image correction and georeferencing based on the real-time navigation data and a camera lens distortion model. The accuracy of the georeferencing algorithm was better than 90 cm according to a series test. The accuracy that has been achieved indicates that, not only is the position solution good, but the attitude error is extremely small. The waypoints planning for UAV flight was investigated. It suggested that a 16.5% forward overlap and a 15% lateral overlap were required to avoi
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)
Rosenberg, S.; Watkins, N. W.; Chapman, S.
2008-12-01
Space plasma physics provides an important arena for the study of natural hazards, because of the threat posed by space weather to space-based and ground based communications and other infrastructure. Extreme fluctuations are thus of interest, and there is by now abundant evidence for scaling in many quantities in the coupled solar-terrestrial system (solar wind, magnetosphere and ionosphere). Direct physical explanations for scaling have been sought through descriptions such as low dimensional chaos, intermittent turbulence (IT) and self-organised criticality (SOC). We have however advocated consideration of a complementary approach (Watkins [NPG, 2002]; Watkins et al. [Space Science Reviews, 2005]). This is the use of deliberately oversimplified mathematical "testbeds" to separate the proprties of the diagnostics used to infer IT or SOC from those of the models themselves. To demonstrate the need for this we consider a recent claim by Uritsky et al ([PRL, 2007]; U07) of direct observational evidence for the coexistence of SOC and IT in the magnetized plasma of the solar corona. By analyzing two dimensional (2D) EUV snapshots (typically 3-4000) of the solar corona, U07 found coexisting power law avalanche statistics and multiscaling of the structure functions. Avalanches were defined by "bursts" for which the signal exceeded a given threshold. These properties were asserted to be robust signatures of SOC and IT respectively. U07 took their coexistence to imply new physics with elements of both SOC and IT. We first point out that U07 assumed that their chosen signatures were unique to SOC and IT. We show however i) that a standard 1D multifractal model of IT, the p-model, straightforwardly generates U07's IT and SOC signatures simultaneously, and ii)that a stochastic process, linear fractional stable motion or LFSM can give the IT signatures and nonlinearity in the structure functions. We infer that not only may it not be necessary to invoke SOC to explain U07's observations, but also that our result has wider implications, which will be discussed.
NASA Astrophysics Data System (ADS)
Koffi, E. N.; Rayner, P. J.; Norton, A. J.; Frankenberg, C.; Scholze, M.
2015-01-01
We investigate the utility of satellite measurements of chlorophyll fluorescence (Fs) in constraining gross primary productivity (GPP). We ingest Fs measurements into the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. CCDAS simulates well the patterns of Fs suggesting the combined model is capable of ingesting these measurements. However simulated Fs is insensitive to the key parameter controlling GPP, the carboxylation capacity (Vcmax). Simulated Fs is sensitive to both the incoming absorbed photosynthetically active radiation (aPAR) and leaf chlorophyll concentration both of which are treated as perfectly known in previous CCDAS versions. Proper use of Fs measurements therefore requires enhancement of CCDAS to include and expose these variables.
Inferring orthology and paralogy.
Altenhoff, Adrian M; Dessimoz, Christophe
2012-01-01
The distinction between orthologs and paralogs, genes that started diverging by speciation versus duplication, is relevant in a wide range of contexts, most notably phylogenetic tree inference and protein function annotation. In this chapter, we provide an overview of the methods used to infer orthology and paralogy. We survey both graph-based approaches (and their various grouping strategies) and tree-based approaches, which solve the more general problem of gene/species tree reconciliation. We discuss conceptual differences among the various orthology inference methods and databases, and examine the difficult issue of verifying and benchmarking orthology predictions. Finally, we review typical applications of orthologous genes, groups, and reconciled trees and conclude with thoughts on future methodological developments. PMID:22407712
Hurwitz, S.; Lowenstern, J. B.; Heasler, H.
2007-01-01
We present and analyze a chemical dataset that includes the concentrations and fluxes of HCO3-, SO42-, Cl-, and F- in the major rivers draining Yellowstone National Park (YNP) for the 2002-2004 water years (1 October 2001 - 30 September 2004). The total (molar) flux in all rivers decreases in the following order, HCO3- > Cl- > SO42- > F-, but each river is characterized by a distinct chemical composition, implying large-scale spatial heterogeneity in the inputs of the various solutes. The data also display non-uniform temporal trends; whereas solute concentrations and fluxes are nearly constant during base-flow conditions, concentrations decrease, solute fluxes increase, and HCO3-/Cl-, and SO42-/Cl- increase during the late-spring high-flow period. HCO3-/SO42- decreases with increasing discharge in the Madison and Falls Rivers, but increases with discharge in the Yellowstone and Snake Rivers. The non-linear relations between solute concentrations and river discharge and the change in anion ratios associated with spring runoff are explained by mixing between two components: (1) a component that is discharged during base-flow conditions and (2) a component associated with snow-melt runoff characterized by higher HCO3-/Cl- and SO42-/Cl-. The fraction of the second component is greater in the Yellowstone and Snake Rivers, which host lakes in their drainage basins and where a large fraction of the solute flux follows thaw of ice cover in the spring months. Although the total river HCO3- flux is larger than the flux of other solutes (HCO3-/Cl- ??? 3), the CO2 equivalent flux is only ??? 1% of the estimated emission of magmatic CO2 soil emissions from Yellowstone. No anomalous solute flux in response to perturbations in the hydrothermal system was observed, possibly because gage locations are too distant from areas of disturbance, or because of the relatively low sampling frequency. In order to detect changes in river hydrothermal solute fluxes, sampling at higher 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.
Real-Time Inference of Mental States from Facial Expressions and Upper Body Gestures
Baltrusaitis, Tadas
We present a real-time system for detecting facial action units and inferring emotional states from head and shoulder gestures and facial expressions. The dynamic system uses three levels of inference on progressively ...
NASA Astrophysics Data System (ADS)
Takahashi, N.; Kodaira, S.; Miura, S.; Sato, T.; Yamashita, M.; No, T.; Takizawa, K.; Kaiho, Y.; Kaneda, Y.
2008-12-01
The Izu-Ogasawara-Mariana (IBM) arc-backarc system has continued the crustal growth through crustal thickening by magmatic activities and crustal thinning by backarc opening. Tatsumi et al (2008) proposed petrological crustal growth model started from basaltic magmas rising from the slab, and showed the consistency with the seismic velocity model. Although crustal growth by the crustal thickening are modeled, crustal structural change by the backarc opening are not still unknown yet. The Shikoku Basin and Parece Vela Basin were formed by the backarc opening during approximately 15-30 Ma. Since 6 Ma, the Mariana Trough has opened and the stage already moved to spreading process from rifting process. In the northern Izu-Ogasawara arc, the Sumisu rift is in the initial rifting stage. Therefore, understanding of the crustal change by the backarc opening from rifting to spreading is indispensable to know the crustal growth of whole Izu-Ogasawara-Mariana island arc. Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has carried out seismic studies using a multichannel reflection survey system and ocean bottom seismographs (OBSs) around the IBM arc since 2003 (Takahashi et al., 2007; Kodaira et al., 2007; Takahashi et al., 2008; Kodaira et al., 2008). We already obtained eight P-wave velocity models across the IBM arc and these structures record the crustal structural change during the backarc opening process from the rifting stage to the spreading stage. As the results, we identified characteristics of the crustal structural change accompanied with backarc opening as follows. (1) Beneath the initial rifting stage without normal faults, for example, in the northern tip of the Mariana Trough, crustal thickening are identified. (2) Beneath the initial rifting stage with normal faults, for example, in the Sumisu Rift, the crustal thickness is almost similar to that beneath the volcanic front. Although an existence of the crust-mantle transition layer with velocity of 7.5-7.7 km/s below the Moho are similar to that beneath the volcanic front, the thickening of a 6.5-6.8 km/s-layer and the thinning of the middle crust with velocity of 6.0- 6.5 km/s are distinctive. (3) Beneath the advanced rifting stage, for example, in the Ogasawara Trough and the Nishinoshima Trough, the crustal thickness is approximately 10-15 km, thicker than that of typical oceanic crust and thinner than that of the arc crust. In addition, the velocity of the lower crust is ~7.5 km/s , and commonly faster than that beneath the volcanic arc. (4) The lower crust with high velocity of ~7.5 km/s beneath the advanced rifting region has a Vp/Vs ratio of ~1.8. This suggests that the origin of the high velocity lower crust is not serpentinized mantle but the mafic crustal materials.
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.
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
Identification of crack profiles using genetic programming and fuzzy inference
Fumio Kojima; Naoyuki Kubota; Setsuo Hashimoto
2001-01-01
This paper deals with a quantitative nondestructive evaluation in eddy current testing for steam generator tubes of nuclear power plants by using genetic programming (GP) and fuzzy inference system. Defects can be detected as a probe impedance trajectory by scanning a pancake type probe coil. An inference system is proposed for identifying the defect shape inside and\\/or outside tubes. GP
Type Inference with Constrained Types Martin Sulzmann \\Lambda
Odersky, Martin
, Germany fodersky,wehrg@ira.uka.de Abstract In this paper we present a general framework HM(X) for Hindley/Milner style type systems with constraints. We give a generic type inference algorithm for HM(X). Under an extension of our HM(X) systems which deals with subtyping. In particular, the type inference algorithm
Inference methods Probabilistic methods
Albert, Réka
Conditional independencies #12;Continued... · Find joint distribution A set of local joint probabilityInference methods · Probabilistic methods Clustering analysis Data mining Bayesian networks analysis · Pair-wise correlation analysis Time-series data spatial data · Gene co-expression network
Inference methods Probabilistic methods
Albert, Réka
distribution A set of local joint probability distributions that statistically convey these relationshipsInference methods · Probabilistic methods Clustering analysis Data mining Bayesian networks analysis · Pair-wise correlation analysis Time-series data spatial data · Gene co-expression network
Perception as Unconscious Inference
Hatfield, Gary
such experience does). Nor does such experience seem on the face of it to be the product of reasoning or inference the table, and you experience its smooth motion before it floats out of sight. The aspects of visual. All of these elements of our visual experience, whether characterized here as phenomenal or cognitive
Page Jr., C. David
colleague, as open secrets" within the probabilistic inference community. This document is addressed- ities by manipulating the secondary structure. In this document, we provide a self-contained, procedural that are scattered throughout the liter- ature. We articulate undocumented, open secrets" that are vital to producing
Distributed inference : combining variational inference with distributed computing
Calabrese, Chris, M. Eng. Massachusetts Institute of Technology
2013-01-01
The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will ...
Wilamowski, Bogdan Maciej
such as neural networks and fuzzy systems [W02]. In this chapter, neuro-fuzzy system [WJK99], as the combination of fuzzy system and neural networks, will be introduced, and compared with classic fuzzy systems, based if a nonlinear characteristic of the system changes with time and there is a need for an adaptive change
Approximate Bayesian Inference Approximate Bayesian Inference for Survival
Steinsland, Ingelin
Approximate Bayesian Inference Approximate Bayesian Inference for Survival Models Rupali Akerkar1 Inference Introduction Outline Basic idea Survival model Present survival model as a latent Gaussian model Basic idea Survival model Present survival model as a latent Gaussian model Apply INLA Verify results
Amin Janghorbani; Abdollah Arasteh; Mohammad Hassan Moradi
2010-01-01
Predicting the future behavior of human's biosignals can help clinicians to prevent occurrence of physiological disorders such as hypotension, hypertension, epilepsy, etc. In addition this prediction helps clinicians to buy some time in order to select a more effective treatment for physiological disorders without exposing the patient to additional risks of delay in receiving treatment. In this paper a local
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model.
Tzafestas, S G; Zikidis, K C
2001-01-01
NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each fuzzy rule is a linear equation. Structure identification is performed by a fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rule splitting and adding procedures. The well known delta rule continuously performs parameter identification on both premise and consequence parameters. Simulation results indicate the potential of the algorithm. It is worth noting that NeuroFAST achieves a remarkable performance in the Box and Jenkins gas furnace process, outperforming all previous approaches compared. PMID:18244844