Neuro-fuzzy models in pattern recognition
Mitra, Sunanda; Kim, Yong Soo
1993-12-01
Research in the last decade emphasized the potential of designing adaptive pattern recognition classifiers based on algorithms using multi-layered artificial neural nets. The greatest potential in such endeavors was anticipated to be not only in the adaptivity but also in the high-speed processing through massively parallel VLSI implementation and optical computing. Computational advantages of such algorithms have been demonstrated in a number of papers. Neural networks particularly the self-organizing types have been found quite suitable crisp pattern for clustering of unlabeled datasets. The generalization of Kohonen-type learning vector quantization (LVQ) clustering algorithm to fuzzy LVQ clustering algorithm and its equivalence to fuzzy c-means has been clearly demonstrated recently. On the other hand, Carpenter/Grossberg's ART-type self organizing neural networks have been modified to perform fuzzy clustering by a number of researches in the past few years. The performance of such neuro-fuzzy models in clustering unlabeled data patterns is addressed in this paper. A recent development of a new similarity measure and a new learning rule for updating the centroid of the winning cluster in a fuzzy ART-type neural network is also described. The capability of the above neuro-fuzzy model in better partitioning of datasets into clusters of any shape is demonstrated.
Neuro-fuzzy modeling in bankruptcy prediction
Vlachos D.
2003-01-01
Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.
Neuro-fuzzy modelling of hydro unit efficiency
This paper presents neuro-fuzzy method for modeling of the hydro unit efficiency. The proposed method uses the characteristics of the fuzzy systems as universal function approximates, as well the abilities of the neural networks to adopt the parameters of the membership's functions and rules in the consequent part of the developed fuzzy system. Developed method is practically applied for modeling of the efficiency of unit which will be installed in the hydro power plant Kozjak. Comparison of the performance of the derived neuro-fuzzy method with several classical polynomials models is also performed. (Author)
NEURO-FUZZY MODELLING OF BLENDING PROCESS IN CEMENT PLANT
Dauda Olarotimi Araromi
2015-11-01
Full Text Available The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX model was developed for comparison purpose. ARX model gave high root mean square error (RMSE of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.
Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.
Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla
2014-12-01
This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies. PMID:25406641
Harish, N.; Mandal, S.; Rao, S.; Lokesha
The Adaptive Neuro Fuzzy Inference System (ANFIS) model is constructed using experimental data set to predict the damage level of berm breakwater. Experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory...
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording.
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza
2003-01-01
Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.
Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants
A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
Guillermo Bosque
2014-01-01
Full Text Available In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of them like the neuro-fuzzy systems, and so on, represent a new point of view to deal this kind of problems due to the approximation properties of those systems (universal approximators.This work shows a methodology to develop a tool based on a neuro-fuzzy system of ANFIS (Adaptive Neuro-Fuzzy Inference System type with piecewise multilinear (PWM behaviour (introducing some restrictions on the membership functions -triangular- chosen in the ANFIS system. The obtained tool is named PWM-ANFIS Tool, that allows modelize a n-dimensional system with one output and, also, permits a comparison between the neuro-fuzzy system modelized, a purely PWM-ANFIS model, with a generic ANFIS (Gaussian membership functions modelized with the same tool. The proposed tool is an efficient tool to deal non-linearly complicated systems.Keywords: ANFIS model, Function approximation, Matlab environment, Neuro-Fuzzy CAD tool, Neuro-Fuzzy modelling.
Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow
Hasiloglu, Abdulsamet [Department of Electronics and Telecommunications Engineering, Engineering Faculty, Ataturk University, Erzurum (Turkey); Yilmaz, Mehmet; Comakli, Omer [Department of Mechanical Engineering, Engineering Faculty, Ataturk University, Erzurum (Turkey); Ekmekci, Ismail [Department of Mechanical Engineering, Engineering Faculty, Sakarya University, Sakarya (Turkey)
2004-11-01
The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of transient heat transfer. An ANFIS has been applied for the transient heat transfer in thermally and simultaneously developing circular duct flow, subjected to a sinusoidally varying inlet temperature. The experiments covered Reynolds numbers in the 2528{<=}Re{<=}4265 range and inlet heat input in the 0.01{<=}{beta}{<=}0.96 Hz frequency range. The accuracy of predictions and the adaptability of the ANFIS were examined, and good predictions were achieved for the temperature amplitudes of the transient heat transfer in thermally and simultaneously developing circular duct flow. The results show that the neuro-fuzzy can be used for modeling transient heat transfer in ducts. The results obtained with the ANFIS are also compared to those of a multiple linear regression and a neural network with a multi-layered feed-forward back-propagation algorithm. (authors)
Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles
Shen, WX; Chan, CC; Lo, EWC; Chau, KT
2002-01-01
This paper proposes and implements a new method for the estimation of the battery residual capacity (BRC) for electric vehicles (EVs). The key of the proposed method is to model the EV battery by using the adaptive neuro-fuzzy inference system. Different operating profiles of the EV battery are investigated, including the constant current discharge and the random current discharge as well as the standard EV driving cycles in Europe, the U.S., and Japan. The estimated BRCs are directly compare...
A Neuro-Fuzzy Technique for Implementing the Half-Adder Circuit Using the CANFIS Model
Lakra, Sachin; Prasad, T. V.; Sharma, Deepak; Atrey, Shree Harsh; Sharma, Anubhav
2012-01-01
A Neural Network, in general, is not considered to be a good solver of mathematical and binary arithmetic problems. However, networks have been developed for such problems as the XOR circuit. This paper presents a technique for the implementation of the Half-adder circuit using the CoActive Neuro-Fuzzy Inference System (CANFIS) Model and attempts to solve the problem using the NeuroSolutions 5 Simulator. The paper gives the experimental results along with the interpretations and possible appl...
Alasha' ary, Haitham; Moghtaderi, Behdad; Page, Adrian; Sugo, Heber [Priority Research Centre for Energy, Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, the University of Newcastle, Callaghan, Newcastle, NSW 2308 (Australia)
2009-07-15
The Masonry Research Group at The University of Newcastle, Australia has embarked on an extensive research program to study the thermal performance of common walling systems in Australian residential buildings by studying the thermal behaviour of four representative purpose-built thermal test buildings (referred to as 'test modules' or simply 'modules' hereafter). The modules are situated on the university campus and are constructed from brick veneer (BV), cavity brick (CB) and lightweight (LW) constructions. The program of study has both experimental and analytical strands, including the use of a neuro-fuzzy approach to predict the thermal behaviour. The latter approach employs an experimental adaptive neuro-fuzzy inference system (ANFIS) which is used in this study to predict the room (indoor) temperatures of the modules under a range of climatic conditions pertinent to Newcastle (NSW, Australia). The study shows that this neuro-fuzzy model is capable of accurately predicting the room temperature of such buildings; thus providing a potential computationally efficient and inexpensive predictive tool for the more effective thermal design of housing. (author)
Achiche, S.; Shlechtingen, M.; Raison, M.;
2016-01-01
This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped with...... influence of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that ANFIS models can be used to set up reliable force predictors for dynamical loaded flexible structures, when a certain degree of inaccuracy is accepted. Furthermore...
A PROPOSED NEURO-FUZZY MODEL FOR ADULT ASTHMA DISEASE DIAGNOSIS
Somdatta Patra
2013-02-01
Full Text Available The task of medical diagnosis with the help different intelligent system techniques is always crucial because it require high level of accuracy and less time consumption in decision making. Among all other AI techniques Artificial Neural Networks (ANN as a tool for medical diagnosis has become the most popular in last few decades due to its flexibility and accuracy. ANN was developed after getting the inspiration from biological neurons. There are various diseases that are still needed to be diagnosed. Among many other critical diseases like cancer, thyroid disorder, diabetes, heart diseases, neuro diseases, asthma disease was also tried to bediagnosed effectively with various ANN mechanisms by different researchers. Due to various uncertainties about symptoms the study of Neuro-Fuzzy technique in this context became very popular in last few years. Neuro-Fuzzy now-a-days is one of the most advanced technique that is mainly concatenation of two model-neural networks and the fuzzy logic. In this model various parameters are used that are much crucial if ill-chosen and may led to failure of the whole system. Recent trend in analysis is following this model for advanced expert work. In this study an enhanced Neuro-fuzzy model has been proposed for the proper diagnosis of adult Asthma disease and to foster the proper aid or medication to the patients and make physicians alert for the upcoming disease pattern otherwise they may lack in the process of providing improper medication at right time. In the first phase data collected from various hospitals are used to train by three different types of learning of ANN like ANN with Self Organizing Maps (SOM, ANN with Learning Vector Quantization (LVQ and ANN with Backpropagation Algorithm (BPA through NF tool for much accurate result. In the second phase fuzzy rule base is applied to the classified data for the diagnosis of the disease.
Alves, Antonio Carlos Pinto Dias
2000-09-01
A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)
Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model
Acampora, Giovanni; Brown, David; Rees, Robert C.
2016-01-01
The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR
Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.
Georgina Cosma
Full Text Available The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA level, the biopsy most common tumor pattern (Primary Gleason pattern and the second most common tumor pattern (Secondary Gleason pattern in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD or Extra-Prostatic Disease (ED using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC, with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812. The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-10-01
The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications. PMID:25426427
System identification of smart structures using a wavelet neuro-fuzzy model
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.
Gani, Abdullah; Mohammadi, Kasra; Shamshirband, Shahaboddin; Khorasanizadeh, Hossein; Seyed Danesh, Amir; Piri, Jamshid; Ismail, Zuraini; Zamani, Mazdak
2016-08-01
The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m2, 0.50-0.71 kWh/m2, and 0.78-0.91, respectively.
Gani, Abdullah; Mohammadi, Kasra; Shamshirband, Shahaboddin; Khorasanizadeh, Hossein; Seyed Danesh, Amir; Piri, Jamshid; Ismail, Zuraini; Zamani, Mazdak
2015-06-01
The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m2, 0.50-0.71 kWh/m2, and 0.78-0.91, respectively.
Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou
2011-01-01
Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.
Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling
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.
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
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza;
2013-01-01
This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS...
Nonlinear Modeling and Neuro-Fuzzy Control of PEMFC
无
2005-01-01
The proton exchange membrane generation technology is highly efficient, and clean and is considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online.This paper analyzed the characters of the PEMFC; and used the approach and self-study ability of artificial neural networks to build the model of nonlinear system, and adopted the adaptive neural-networks fuzzy infer system to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusted the model parameters to control online. The model and control were implemented in SIMULINK environment.The results of simulation show the test data and model have a good agreement. The model is useful for the optimal and real time control of PEMFC system.
Modeling of a HTPEM fuel cell using Adaptive Neuro-Fuzzy Inference Systems
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Sahlin, Simon Lennart
2015-01-01
In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) model of the voltage of a fuel cell is developed. The inputs of this model are the fuel cell temperature, current density and the carbon monoxide concentration of the anode supply gas. First an identification experiment which spans the...... expected operating range of the fuel cell is performed in a test station. The data from this experiment is then used to train ANFIS models with 2, 3, 4 and 5 membership functions. The performance of these models is then compared and it is found that using 3 membership functions provides the best compromise...... between performance and fast model evaluation. This model has a mean absolute error of 0.70%. It is concluded that the developed ANFIS model is suitable for optimization of fuel cell systems and as the steady state component in larger dynamic system models....
Adaptive Traffic Signalization Model using Neuro-Fuzzy Controllers
Devesh Batra; Pragya Verma
2014-01-01
Current traffic lights are pre-programmed and use daily signal timing schedules, which contribute to traffic congestion and delay. Thus, with the increase in the number of vehicles on road, need for adaptive signal technology arises which has the potential to adjust the timing of red, yellow and green lights in order to accommodate changing traffic patterns and ease traffic congestion. In this paper, we present a model for adaptive traffic signalization, which uses fuzzy neura...
Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm
Bouzaida, Sana; Sakly, Anis; M'Sahli, Faouzi
2014-01-01
This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.
Adaptive Traffic Signalization Model using Neuro-Fuzzy Controllers
Devesh Batra*
2014-07-01
Full Text Available Current traffic lights are pre-programmed and use daily signal timing schedules, which contribute to traffic congestion and delay. Thus, with the increase in the number of vehicles on road, need for adaptive signal technology arises which has the potential to adjust the timing of red, yellow and green lights in order to accommodate changing traffic patterns and ease traffic congestion. In this paper, we present a model for adaptive traffic signalization, which uses fuzzy neural network for designing traffic signal controller. The controllers use vehicle detectors in order to detect the number of incoming vehicles. Based on the number of approaching vehicles, the current signal phase is either extended or terminated. The traffic volume at one particular region in an intersection is compared with that in the competing regions of the same intersection. The decision made is thus robust and results in less congestion and delays.
Blind identification of threshold auto-regressive model for machine fault diagnosis
LI Zhinong; HE Yongyong; CHU Fulei; WU Zhaotong
2007-01-01
A blind identification method was developed for the threshold auto-regressive (TAR) model. The method had good identification accuracy and rapid convergence, especially for higher order systems. The proposed method was then combined with the hidden Markov model (HMM) to determine the auto-regressive (AR) coefficients for each interval used for feature extraction, with the HMM as a classifier. The fault diagnoses during the speed-up and speed- down processes for rotating machinery have been success- fully completed. The result of the experiment shows that the proposed method is practical and effective.
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...
NEURO FUZZY LINK BASED CLASSIFIER FOR THE ANALYSIS OF BEHAVIOR MODELS IN SOCIAL NETWORKS
Indira Priya Ponnuvel; Ghosh Dalim Kumar; Kannan Arputharaj; Ganapathy Sannasi
2014-01-01
In this study, a new link based classifier using neuro fuzzy logic has been proposed for analyzing the social behavior based on Weblog dataset. In this system, data are processed using a multistage structure. This system provides a diagnosis using a neuro fuzzy link based classifier that analyses the user’s behavior to specific diagnostic categories based on their cluster category in social networks. It uses random walks method to organize the labels. Since the links present in the soci...
Amiri, Arshia; Bakhshoodeh, Mohamad; Najafi, Bahaeddin
2011-01-01
This paper, we studied the ability of geostatistical models (ordinary kriging (OK) and Inverse distance weighting (IDW)), adaptive neuro-fuzzy inference system (ANFIS) and Winter method for prediction of seasonality in prices of potatoes and onions in Iran over the seasonal period 1986_2001. Results show that the best estimators in order are winter method, ANFIS and geostatistical methods. The results indicate that Winter and ANFIS had powerful results for prediction the prices while geostati...
Time-varying parameter auto-regressive models for autocovariance nonstationary time series
无
2009-01-01
In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.
Time-varying parameter auto-regressive models for autocovariance nonstationary time series
FEI WanChun; BAI Lun
2009-01-01
In this paper,autocovariance nonstationary time series is clearly defined on a family of time series.We propose three types of TVPAR (time-varying parameter auto-regressive) models:the full order TVPAR model,the time-unvarying order TVPAR model and the time-varying order TVPAR model for autocovariance nonstationary time series.Related minimum AIC (Akaike information criterion) estimations are carried out.
TIAN Lin-ya; HUA Xi-sheng
2007-01-01
To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm,indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.
Zhao Haijun; Ma Yan; Huang Xiaohong; Su Yujie
2008-01-01
Predicting heartbeat message arrival time is crucial for the quality of failure detection service over internet. However, internet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model's coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous (ARX) model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.
Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat
2016-05-01
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.
Forecasting Energy Consumption Using Fuzzy Transform and Local Linear Neuro Fuzzy Models
Hossein Iranmanesh
2011-12-01
Full Text Available This paper proposes a hybrid approach based on local linear neuro fuzzy (LLNF model and fuzzytransform (F-transform, termed FT-LLNF, for prediction of energy consumption. LLNF models arepowerful in modelling and forecasting highly nonlinear and complex time series. Starting from an optimallinear least square model, they add nonlinear neurons to the initial model as long as the model's accuracyis improved. Trained by local linear model tree learning (LOLIMOT algorithm, the LLNF models providemaximum generalizability as well as the outstanding performance. Besides, the recently introducedtechnique of fuzzy transform (F-transform is employed as a time series pre-processing method. Thetechnique of F-transform, established based on the concept of fuzzy partitions, eliminates noisy variationsof the original time series and results in a well-behaved series which can be predicted with higheraccuracy. The proposed hybrid method of FT-LLNF is applied to prediction of energy consumption in theUnited States and Canada. The prediction results and comparison to optimized multi-layer perceptron(MLP models and the LLNF itself, revealed the promising performance of the proposed approach forenergy consumption prediction and its potential usage for real world applications.
Probing turbulence intermittency via Auto-Regressive Moving-Average models
Faranda, Davide; Dubrulle, Berengere; Daviaud, Francois
2014-01-01
We suggest a new approach to probing intermittency corrections to the Kolmogorov law in turbulent flows based on the Auto-Regressive Moving-Average modeling of turbulent time series. We introduce a new index $\\Upsilon$ that measures the distance from a Kolmogorov-Obukhov model in the Auto-Regressive Moving-Average models space. Applying our analysis to Particle Image Velocimetry and Laser Doppler Velocimetry measurements in a von K\\'arm\\'an swirling flow, we show that $\\Upsilon$ is proportional to the traditional intermittency correction computed from the structure function. Therefore it provides the same information, using much shorter time series. We conclude that $\\Upsilon$ is a suitable index to reconstruct the spatial intermittency of the dissipation in both numerical and experimental turbulent fields.
Rezazadeh, S.; Mirzaee, I. [Urmia Univ., Urmia (Iran, Islamic Republic of); Mehrabi, M. [University of Pretoria, Pretoria (South Africa)
2012-11-15
In this paper, an adaptive neuro fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm{sup 2}) is modeled to the variation of pressure at the cathode side P{sup C} (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient {alpha}{sup an}, relative humidity of inlet fuel RH{sup a} and relative humidity of inlet air RH{sup c} which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states.
Vipan K Sohpal
2014-06-01
Full Text Available Transesterification of Jatropha curcus for biodiesel production is a kinetic control process, which is complex in nature and controlled by temperature, the molar ratio, mixing intensity and catalyst process parameters. A precise choice of catalyst is required to improve the rate of transesterification and to simulate the kinetic study in a batch reactor. The present paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS approach to model and simulate the butyl ester production using alkaline catalyst (NaOH. The amounts of catalyst and time for reaction have been used as the model’s input parameters. The model is a combination of fuzzy inference and artificial neural network, including a set of fuzzy rules which have been developed directly from experimental data. The proposed modeling approach has been verified by comparing the expected results with the practical results which were observed and obtained through a batch reactor operation. The application of the ANFIS test shows which amount of catalyst predicted by the proposed model is suitable and in compliance with the experimental values at 0.5% level of significance.
An adaptive neuro fuzzy model for estimating the reliability of component-based software systems
Kirti Tyagi
2014-01-01
Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
Møller, Niels Framroze
This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its...... demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems
Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)
2008-01-15
The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the modelling of ground-coupled heat pump (GCHP) system. The GCHP system connected to a test room with 16.24 m{sup 2} floor area in Firat University, Elazig (38.41 N, 39.14 E), Turkey, was designed and constructed. The heating and cooling loads of the test room were 2.5 and 3.1 kW at design conditions, respectively. The system was commissioned in November 2002 and the performance tests have been carried out since then. The average performance coefficients of the system (COPS) for horizontal ground heat exchanger (GHE) in the different trenches, at 1 and 2 m depths, were obtained to be 2.92 and 3.2, respectively. Experimental performances were performed to verify the results from the ANFIS approach. In order to achieve the optimal result, several computer simulations have been carried out with different membership functions and various number of membership functions. The most suitable membership function and number of membership functions are found as Gauss and 2, respectively. For this number level, after the training, it is found that root-mean squared (RMS) value is 0.0047, and absolute fraction of variance (R{sup 2}) value is 0.9999 and coefficient of variation in percent (cov) value is 0.1363. This paper shows that the values predicted with the ANFIS, especially with the hybrid learning algorithm, can be used to predict the performance of the GCHP system quite accurately. (author)
Adaptive Neuro-Fuzzy Model Tuning for Early-Warning of Financial Crises
Erika SZTOJANOV; Stamatescu, Grigore
2015-01-01
The paper introduces an early-warning method using multiple financial crises indicators which outputs relevant alerts compared to a precise indication of crisis inception, serving as an effective tool for decision makers. By leveraging fuzzy logic techniques, we design a multi-level fuzzy decision support system based on the evolution of credit growth, housing prices and GDP gap. A neuro-fuzzy approach allows fine tuning of the individual fuzzy sub-systems towards adaptive structures which ca...
NEURO FUZZY LINK BASED CLASSIFIER FOR THE ANALYSIS OF BEHAVIOR MODELS IN SOCIAL NETWORKS
Indira Priya Ponnuvel
2014-01-01
Full Text Available In this study, a new link based classifier using neuro fuzzy logic has been proposed for analyzing the social behavior based on Weblog dataset. In this system, data are processed using a multistage structure. This system provides a diagnosis using a neuro fuzzy link based classifier that analyses the user’s behavior to specific diagnostic categories based on their cluster category in social networks. It uses random walks method to organize the labels. Since the links present in the social network graph frequently represent relationships among the users with respect to social contacts and behaviours, this work observes the links of the graph in order to identify the relationships represented in the graph between the users of the social network based on some new social network metrics and the past behaviour of the users. This work is useful to provide connection between consolidated features of users based on network data and also using the traditional metrics used in the analysis of social network users. From the experiments conducted in this research work, it is observed that the proposed work provides better classification accuracy due to the application of neuro fuzzy classification method in link analysis.
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2013-01-01
In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... hydrogen, which is difficult and energy consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and Adaptive Neuro-Fuzzy Inference System models of the...... reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...
NEURO FUZZY MODEL FOR FACE RECOGNITION WITH CURVELET BASED FEATURE IMAGE
SHREEJA R,
2011-06-01
Full Text Available A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometric techniques such as fingerprint or iris recognition systems. Every face has approximately 80 nodal points like (Distance between the eyes, Width of the nose etc.The basic face recognition system capture the sample, extract feature, compare template and perform matching. In this paper two methods of face recognition are compared- neural networks and neuro fuzzy method. For this curvelet transform is used for feature extraction. Feature vector is formed by extracting statistical quantities of curve coefficients. From the statistical results it is concluded that neuro fuzzy method is the better technique for face recognition as compared to neural network.
Karami Alireza; Afiuni-Zadeh Somaieh
2013-01-01
One of the most important characters of blasting, a basic step of surface mining, is rock fragmentation because it directly effects on the costs of drilling and economics of the subsequent operations of loading, hauling and crushing in mines. Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF) show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks. We developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80%passing size (K80) of Golgohar iron mine of Sirjan, Iran. Comparing the results of ANFIS and RBF models shows that although the statistical parame-ters RBF model is acceptable but ANFIS proposed model is superior and also simpler because ANFIS model is constructed using only two input parameters while seven input parameters used for construction of RBF model.
Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R2), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.
Bilgehan, Mahmut
2011-03-01
In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the evaluation of relationships between concrete compressive strength and ultrasonic pulse velocity (UPV) values using the experimental data obtained from many cores taken from different reinforced concrete structures having different ages and unknown ratios of concrete mixtures. A comparative study is made using the neural nets and neuro-fuzzy (NF) techniques. Statistic measures were used to evaluate the performance of the models. Comparing of the results, it is found that the proposed ANFIS architecture with Gaussian membership function is found to perform better than the multilayer feed-forward ANN learning by backpropagation algorithm. The final results show that especially the ANFIS modelling may constitute an efficient tool for prediction of the concrete compressive strength. Architectures of the ANFIS and neural network established in the current study perform sufficiently in the estimation of concrete compressive strength, and particularly ANFIS model estimates closely follow the desired values. Both ANFIS and ANN techniques can be used in conditions where too many structures are to be examined in a restricted time. The presented approaches enable to practically find concrete strengths in the existing reinforced concrete structures, whose records of concrete mixture ratios are not available or present. Thus, researchers can easily evaluate the compressive strength of concrete specimens using UPV and density values. These methods also contribute to a remarkable reduction in the computational time without any significant loss of accuracy. A comparison of the results clearly shows that particularly the NF approach can be used effectively to predict the compressive strength of concrete using UPV and density values. In addition, these model architectures can be used as a nondestructive procedure for health monitoring of
Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system
Musafere, F.; Sadhu, A.; Liu, K.
2016-01-01
In the last few decades, structural health monitoring (SHM) has been an indispensable subject in the field of vibration engineering. With the aid of modern sensing technology, SHM has garnered significant attention towards diagnosis and risk management of large-scale civil structures and mechanical systems. In SHM, system identification is one of major building blocks through which unknown system parameters are extracted from vibration data of the structures. Such system information is then utilized to detect the damage instant, and its severity to rehabilitate and prolong the existing health of the structures. In recent years, blind source separation (BSS) algorithm has become one of the newly emerging advanced signal processing techniques for output-only system identification of civil structures. In this paper, a novel damage detection technique is proposed by integrating BSS with the time-varying auto-regressive modeling to identify the instant and severity of damage. The proposed method is validated using a suite of numerical studies and experimental models followed by a full-scale structure.
Day-ahead prediction using time series partitioning with Auto-Regressive model
Dennis Cheruiyot Kiplangat
2016-08-01
Full Text Available Wind speed forecasting has received a lot of attention in the recent past from researchers due to its enormous benefits in the generation of wind power and distribution. The biggest challenge still remains to be accurate prediction of wind speeds for efficient operation of a wind farm. Wind speed forecasts can be greatly improved by understanding its underlying dynamics. In this paper, we propose a method of time series partitioning where the original 10 minutes wind speed data is converted into a twodimensional array of order (N x 144 where N denotes the number of days with 144 the daily 10-min observations. Upon successful time series partitioning, a point forecast is computed for each of the 144 datasets extracted from the 10 minutes wind speed observations using an Auto-Regressive (AR process which is then combined together to give the (N+1st day forecast. The results of the computations show significant improvement in the prediction accuracy when AR model is coupled with time series partitioning.
Steganalysis of LSB Image Steganography using Multiple Regression and Auto Regressive (AR Model
Souvik Bhattacharyya
2011-07-01
Full Text Available The staggering growth in communication technologyand usage of public domain channels (i.e. Internet has greatly facilitated transfer of data. However, such open communication channelshave greater vulnerability to security threats causing unauthorizedin- formation access. Traditionally, encryption is used to realizethen communication security. However, important information is notprotected once decoded. Steganography is the art and science of communicating in a way which hides the existence of the communication.Important information is ﬁrstly hidden in a host data, such as digitalimage, text, video or audio, etc, and then transmitted secretly tothe receiver. Steganalysis is another important topic in informationhiding which is the art of detecting the presence of steganography. Inthis paper a novel technique for the steganalysis of Image has beenpresented. The proposed technique uses an auto-regressive model todetect the presence of the hidden messages, as well as to estimatethe relative length of the embedded messages.Various auto regressiveparameters are used to classify cover image as well as stego imagewith the help of a SVM classiﬁer. Multiple Regression analysis ofthe cover carrier along with the stego carrier has been carried outin order to ﬁnd out the existence of the negligible amount of thesecret message. Experimental results demonstrate the effectivenessand accuracy of the proposed technique.
Neuro-Fuzzy Phasing of Segmented Mirrors
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.
Yea-Kuang Chan; Jyh-Cherng Gu
2012-01-01
Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find an accurate model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycles to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of the Kuosheng NPP in Taiwan. Plant operation data obtained from Kuosheng NPP between 2006 and 2011 were ...
Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi
2007-04-01
SummaryModeling of rainfall-runoff dynamics is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity for calibrating the nonlinear relationships inherent in the rainfall-runoff process. In this study, the advantages of artificial neural networks and neuro-fuzzy system in continuous modeling of the daily and hourly behaviour of runoff were examined. Three different adaptive techniques were constructed and examined namely, Levenberg-Marquardt feed forward neural network, Bayesian regularization feed forward neural network, and neuro-fuzzy. In addition, the effects of data transformation on model performance were also investigated. This was done by examining the performance of the three network architectures and training algorithms using both raw and transformed data. Through inspection of the results it was found that although the model built on transformed data outperforms the model built on raw data, no significant differences were found between the forecast accuracies of the three examined models. A detailed comparison of the overall performance indicated that the neuro-fuzzy model performed better than both the Levenberg-Marquardt-FFNN and the Bayesian regularization-FFNN. In order to enable users to process the data easily, a graphic user interface (GUI) was developed. This program allows users to process the rainfall-runoff data, to train/test the model using various input options and to visualize results.
Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model
Kerim Goztepe
2013-09-01
Full Text Available Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.Defence Science Journal, 2013, 63(5, pp.497-501, DOI:http://dx.doi.org/10.14429/dsj.63.3716
Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model
Kerim Goztepe
2014-07-01
Full Text Available Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.
F. Sdao
2013-02-01
Full Text Available 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
Winters Jack M
2005-06-01
Full Text Available Abstract 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
Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm
Hong, Yoon-Seok Timothy; White, Paul A.
2009-01-01
This paper introduces the dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system with on-line and local learning algorithm for complex dynamic hydrological modeling tasks. Our DNFLMS is aimed to implement a fast training speed with the capability of on-line simulation where model adaptation occurs at the arrival of each new item of hydrological data. The DNFLMS applies an on-line, one-pass, training procedure to create and update fuzzy local models dynamically. The extended Kalman filtering algorithm is then implemented to optimize the parameters of the consequence part of each fuzzy model during the training phase. Local generalization in the DNFLMS is employed to optimize the parameters of each fuzzy model separately, region-by-region, using subsets of training data rather than all training data. The proposed DNFLMS is applied to develop a model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatt Cobb hydropower station on spring flow. It is demonstrated that the proposed DNFLMS is superior in terms of model complexity and computational efficiency when compared with models that adopt global generalization such as a multi-layer perceptron (MLP) trained with the back propagation learning algorithm and the well-known adaptive neural-fuzzy system (ANFIS).
Karami Alireza; Afiuni-Zadeh Somaieh
2012-01-01
One of the most important characters of blasting,a basic step of surface mining,is rock fragmentation.It directly effects on the costs of drilling and economics of the subsequent operations of loading,hauling and crushing in mines.Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF)show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks.In this paper we developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80％ passing size (K80) of Golgohar iron ore mine of Sir jan,Iran.Comparing the results of ANFIS and RBF models shows that although the statistical parameters RBF model is acceptable but the ANFIS proposed model is superior and also simpler because the ANFIS model is constructed using only two input parameters while seven input parameters used for construction of the RBF model.
Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.
Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad
2013-05-01
In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model. PMID:23111771
Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)
2009-08-15
The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussian...... iterative schemes. The proposed methods also enjoy good data efficiency since only second order statistics is involved in the computation. When measurement noise is present, a novel Switching Kalman Smoother is incorporated into the EM algorithm, obtaining optimum nonlinear MMSE estimates of the system...
Mehrbakhsh Nilashi, Mohammad Fathian, Mohammad Reza Gholamian, Othman bin Ibrahim
2011-08-01
Full Text Available If companies are to enjoy long-term success in the Internet marketplace, they must effectivelymanage the complex, multidimensional process of building online consumer trust. The onlineenvironment and the quality and usability of websites help the browser and consumer to beattracted and accessible to the information and the product and services available online. In thisPaper a new model would be suggested based on neuro-fuzzy System which depicts some of thehidden relationships between the critical factors such as security, familiarity, and designing in aB2C commercial website on other hand, and the competitive factor to other competitors on otherhand. Then, the impacts of these factors on purchasing decision of consumers in B2Ccommercial websites are extracted. We are going to find the impact of these factors on thedecision-making process of people to buy through the B2C commercial websites, and we also willanalyze how these factors influence the results of the B2C trading. The study also provides adevice for sellers to improve their commercial websites. Two questionnaires were used in thisstudy. The first questionnaire was developed for e-commerce experts, and the second one wasdesigned for the customers of commercial websites. Also, Expert Choice is used to determine thepriority of factors in the first questionnaire, and MATLAB and Excel are used for developing theFuzzy rules. Finally, the Fuzzy logical kit was use to analyze the generated factors in the model.
Ja’fari A.
2014-01-01
Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.
Neuro-fuzzy generalized predictive control of boiler steam temperature
Chan, CW; Liu, XJ
2006-01-01
Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. This is often difficult to achieve using conventional PI controllers, as power plants are nonlinear and contain many uncertainties. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy netw...
Kurtulus, Bedri; Razack, Moumtaz
2010-02-01
SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.
Oh, Hyun-Joo; Pradhan, Biswajeet
2011-09-01
This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.
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
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
Design of neuro-fuzzy models by evolutionary and gradient-based algorithms
Cabrita, Cristiano Lourenço
2013-01-01
All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of mode...
PORTFOLIO OPTIMIZATION USING NEURO FUZZY SYSTEM IN INDIAN STOCK MARKET
Guna Sekar
2012-05-01
Full Text Available This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through diversification and right investment allocation to the particular stock under uncertainty. To evaluate the performance of forecasting and optimization system, BSE Sensex index of India considered as benchmarks in this study to measure efficient forecasting models. The results show that the proposed Neuro Fuzzy system produces much higher accuracy when compared to other portfolio models.
Modelling Rule- and Experience-Based Expectations Using Neuro-Fuzzy-Systems
Stefan Kooths
1999-01-01
Expectations modelling in macroeconomic theory is often done under restrictive assumptions regarding people's ability to learn and the level of their knowledge. Either it is assumed that people do not learn at all, which justifies the use of simple autoregressive forecasting methods, or the model makers believe that the relevant agents know everything about the (long-term) behaviour of the economic system (rational expectations). Neither of these seems realistically to describe what people re...
Applied to neuro-fuzzy models for signal validation in Angra 1 nuclear power plant
This work develops two models of signal validation in which the analytical redundancy of the monitored signals from an industrial plant is made by neural networks. In one model the analytical redundancy is made by only one neural network while in the other it is done by several neural networks, each one working in a specific part of the entire operation region of the plant. Four cluster techniques were tested to separate the entire region of operation in several specific regions. An additional information of systems' reliability is supplied by a fuzzy inference system. The models were implemented in C language and tested with signals acquired from Angra I nuclear power plant, from its start to 100% of power. (author)
A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system
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. (paper)
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
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…
Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models
Schlechtingen, Meik; Santos, Ilmar
2012-01-01
in graphical and text format. Within the paper examples of real faults are provided, showing the capabilities of the method proposed. The method can be applied both to existing and new built turbines without the need of any additional hardware installation or manufacturers input....... System (ANFIS) models are employed to learn the normal behavior in a training phase, where the component condition can be considered healthy. In the application phase the trained models are applied to predict the target signals, e.g. temperatures, pressures, currents, power output, etc. The behavior...... the component condition Fuzzy Interference System (FIS) structures are used. Based on rules that are established with the prediction error behavior during faults previously experienced and generic rules, the FIS outputs the component condition (green, yellow and red). Furthermore a first diagnosis of the root...
Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach
Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel
2016-01-01
Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.
Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles
无
2003-01-01
This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.
Justesen, Kristian Kjær; Ehmsen, Mikkel Præstholm; Andersen, John;
2012-01-01
charger. The advantages of using a HTPEM methanol reformer is that the high quality waste heat can be used as a system heat input to heat and evaporate the input methanol/water mixture which afterwards is catalytically converted into a hydrogen rich gas usable in the high CO tolerant HTPEM fuel cells....... Creating a fuel cell system able to use a well known and easily distributable liquid fuel such as methanol is a good choice in some applications such as range extenders for electric vehicles as an alternative to compressed hydrogen. This work presents a control strategy called Current Correction......This work presents the experimental study and modelling of a methanol reformer system for a high temperature polymer electrolyte membrane (HTPEM) fuel cell stack. The analyzed system is a fully integrated HTPEM fuel cell system with a DC/DC control output able to be used as e.g. a mobile battery...
A mathematical model of neuro-fuzzy approximation in image classification
Gopalan, Sasi; Pinto, Linu; Sheela, C.; Arun Kumar M., N.
2016-06-01
Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Abdel K.M. Baareh
2006-01-01
Full Text Available Forecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN became quite successful tool to handle time series modeling problem. This paper provides a solution to the forecasting problem of the river flow for two well known Rivers in the USA. They are the Black Water River and the Gila River. The selected ANN models were used to train and forecast the daily flows of the first station no: 02047500, for the Black Water River near Dendron in Virginia and the second station no: 0944200 for the Gila River near Clifton in Arizona. The feed forward network is trained using the conventional back propagation learning algorithm with many variations in the NN inputs. We explored models built using various historical data. The selection process of various architectures and training data sets for the proposed NN models are presented. A comparative study of both ANN and the conventional Auto-Regression (AR model networks indicates that the artificial neural networks performed better than the AR model. Hence, we recommend ANN as a useful tool for river flow forecasting.
Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling
Huang, Lei
2015-01-01
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409
Bootstrapping the portmanteau tests in weak auto-regressive moving average models
Zhu, Ke
2015-01-01
This paper uses a random weighting (RW) method to bootstrap the critical values for the Ljung-Box/Monti portmanteau tests and weighted Ljung-Box/Monti portmanteau tests in weak ARMA models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power GARCH models. Simulation evidence indicates that the weighted portmanteau tests have the power advantage over other existing tests...
G. Ravi Shankar Reddy; Rameshwar Rao
2014-01-01
Time-varying autoregressive (TVAR) model is used for modeling non stationary signals, Instantaneous frequency (IF) and time-varying power spectral density are then extracted from the TVAR parameters. TVAR based Instantaneous frequency (IF) estimation has been shown to perform very well in realistic scenario when IF variation is quick, non-linear and has short data record. In TVAR modeling approach, the time-varying parameters are expanded as linear combinations of a set of basis functions .In...
Hassan Abba Musa; Dr. A. Mohammed
2016-01-01
In current practice, the predictive analysis of stochastic problems encompasses a variety of statistical techniques from modeling, machine, and data mining that analyse current and historical facts to make predictions about future. Therefore, this research uses an AR Model whose codes are incorporated in the MATLAB software to predict possible aero-elastic effects of Lekki Bridge based on its existing parametric data and the conditions around the bridge. It was seen that, the fluc...
Hassan Abba Musa
2016-06-01
Full Text Available In current practice, the predictive analysis of stochastic problems encompasses a variety of statistical techniques from modeling, machine, and data mining that analyse current and historical facts to make predictions about future. Therefore, this research uses an AR Model whose codes are incorporated in the MATLAB software to predict possible aero-elastic effects of Lekki Bridge based on its existing parametric data and the conditions around the bridge. It was seen that, the fluctuating components of the wind velocity as displayed by the fluctuant curve will result in the vibration of the structure, even strengthening the resonance effect of the structure. Therefore, it suggested that, the natural frequency of the bridge should be set aside far from system frequency considering direct parametric excitation of pedestrian or vehicular traffic speed.
DONG Ming
2008-01-01
As a new maintenance method, CBM (condition based maintenance) is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac-tice in industry is effective diagnostics and prognostics. Recently, a pattern recog-nition technique called HMM (hidden Markov model) was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip-ment health management based on auto-regressive hidden semi-Markov model (AR-HSMM). Compared with HMM, AR-HSMM has three advantages: 1)It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2) It can relax observations' independence assumption by accom-modating a link between consecutive observations. 3) It does not follow the unre-alistic Markov chain's memoryless assumption and therefore provides more pow-erful modeling and analysis capability for real problems. To facilitate the computation in the proposed AR-HSMM-based diagnostics and prognostics, new forwardbackward variables are defined and a modified forward-backward algorithm is developed. The evaluation of the proposed methodology was carried out through a real world application case study: health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision-making in equipment health management.
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models.
Tarassenko, L; Villarroel, M; Guazzi, A; Jorge, J; Clifton, D A; Pugh, C
2014-05-01
Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient's face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter. PMID:24681430
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient’s face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter. (paper)
无
2000-01-01
The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner.NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if part of the rules and summation to integrate the fired rules. Expert knowledge from previous process plans is used in determining the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.
P. N. Raghunath
2012-01-01
Full Text Available Problem statement: This study presents the results of ANFIS based model proposed for predicting the performance characteristics of reinforced HSC beams subjected to different levels of corrosion damage and strengthened with externally bonded glass fibre reinforced polymer laminates. Approach: A total of 21 beams specimens of size 150, 250×3000 mm were cast and tested. Results: Out of the 21 specimens, 7 specimens were tested without any corrosion damage (R-Series, 7 after inducing 10% corrosion damage (ASeries and another 7 after inducing 25% corrosion damage (B-Series. Out of the seven specimens in each series, one was tested without any laminate, three specimens were tested after applying 3 mm thick CSM, UDC and WR laminates and another three specimens after applying 5mm thick CSM, UDC and WR laminates. Conclusion/Recommendations: The test results show that the beams strengthened with externally bonded GFRP laminates exhibit increased strength, stiffness, ductility and composite action until failure. An Adaptive Neuro-Fuzzy Inference System (ANFIS model is developed for predicting the study parameters for input values lying within the range of this experimental study.
Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin
2014-01-01
Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. PMID:24566279
Giuseppe Casalino
2013-01-01
Full Text Available Weld imperfections are tolerable defects as stated from the international standard. Nevertheless they can produce a set of drawbacks like difficulty to assembly, reworking, limited fatigue life, and surface imperfections. In this paper Ti6Al4V titanium butt welds were produced by CO2 laser welding. The following tolerable defects were analysed: weld undercut, excess weld metal, excessive penetration, incomplete filled groove, root concavity, and lack of penetration. A neuro-fuzzy model for the prediction and classification of the defects in the fused zone was built up using the experimental data. Weld imperfections were connected to the welding parameters by feed forward neural networks. Then the imperfections were clustered using the C-means fuzzy clustering algorithm. The clusters were named after the ISO standard classification of the levels of imperfection for electron and laser beam welding of aluminium alloys and steels. Finally, a single-value metric was proposed for the assessment of the overall bead geometry quality. It combined an index for each defect and functioned according to the criterion “the-smallest-the-best.”
A New Neuro-Fuzzy Adaptive Genetic Algorithm
ZHU Lili; ZHANG Huanchun; JING Yazhi
2003-01-01
Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.
Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction
Yue Wu
2011-05-01
Full Text Available Fuzzy logic is a rigorous mathematical field, and it provides an effective vehicle for modeling the uncertainty in human reasoning. In fuzzy logic, the knowledge of experts is modeled by linguistic rules represented in the form of IF-THEN logic. Like neural network models such as the multilayer perceptron (MLP and the radial basis function network (RBFN, some fuzzy inference systems (FISs have the capability of universal approximation. Fuzzy logic can be used in most areas where neural networks are applicable. In this paper, we first give an introduction to fuzzy sets and logic. We then make a comparison between FISs and some neural network models. Rule extraction from trained neural networks or numerical data is then described. We finally introduce the synergy of neural and fuzzy systems, and describe some neuro-fuzzy models as well. Some circuits implementations of neuro-fuzzy systems are also introduced. Examples are given to illustrate the cocepts of neuro-fuzzy systems.
Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia
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.
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2014-01-01
In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseou...
Radovanović Milan M.
2015-01-01
Full Text Available In this research we search for a functional dependence between the occurrence of forest fires in the USA and the factors which characterize the solar activity. For this purpose we used several methods (R/S analysis, Hurst index to establish potential links between the influx of some parameters from the sun and the occurrence of forest fires with lag of several days. We found evidence for a connection and developed a prognostic scenario based on the Adaptive neuro-fuzzy interference system (ANFIS technique. This scenario allows the prediction between 79-93% of forest fires. [Projekat Ministarstva nauke Republike Srbije, br. III47007
Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe
A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data
Artificial Neural Networks and Fuzzy Logic models can be combined to exploit learning and generalization capability of the first technique with the approximate reasoning embedded in the second approach. Real-time process signal validation is an application field where the use of this technique can improve the diagnosis of faulty sensors and the identification of outliers in a robust and reliable way. This study implements a fuzzy and possibilistic clustering algorithm to classify the operating region where the validation process has to be performed. The possibilistic approach (rather than probabilistic) allows a 'don't know' classification that results in a fast detection of unforeseen plant conditions or outliers. Specialized Artificial Neural Networks are used for the validation process, one for each fuzzy cluster in which the operating map has been divided. They work concurrently on each signal pattern presented to the system and the overall contribution is weighted according to the membership function of the pattern in each cluster. There are two main advantages in using this technique: the accuracy and generalization capability is increased compared to the case of a single network working in the entire operating region, and the ability to identify abnormal conditions, where the system is not capable to operate with a satisfactory accuracy, is improved. This model has been tested in a simulated environment on a French PWR, to monitor safety-related reactor variables over the entire power-flow operating map. (authors)
Application of Neuro-Fuzzy Techniques for Solar Radiation
W. A. Rahoma
2011-01-01
Full Text Available Problem statement: The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with the use of additional meteorological parameters. After having simulated many different structures of neural networks and trained using measurements as training data, the best structures were selected in order to evaluate their performance in relation with the performance of a neuro-fuzzy system. As the alternative system, ANFIS neuro-fuzzy system was considered, because it combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. ANFIS is trained with the same data. Results: The comparison and the evaluation of both of the systems were done according to their predictions, using several error metrics. Fuzzy model was trained using data of daily solar radiation recorded on a horizontal surface in National Research Institute of Astronomy and Geophysics, Helwan, Egypt (NARIG at ten years (1991-2000. Conclusion: The predicting conclusion indicated that the TS fuzzy model gave a good accuracy of approximately 96% and a root mean square error lower than 6%.
Biyanto, Totok R.
2016-06-01
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO2 emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Neuro-fuzzy predictive control for nonlinear application
CHEN Dong-xiang; WANG Gang; LV Shi-xia
2008-01-01
Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.
Modeling long-term price development by using time-series auto-regression and expert judgments
Shnyrev, Denis
2009-01-01
The objective of the thesis was to create a long term forecast for pine roundwood timber assortment’s price in Finland for the years 2010 until 2020. The forecast’s purpose was to serve as helpful information in strategic business planning of Oy Karelkon LTD throughout the next decade. The theoretical framework for this thesis was based on quantitative forecasting theory and in particular the theory of autoregressive model of order one, or the AR1 model. This model was applied in order t...
The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models
Daniela Spiesová
2014-10-01
Full Text Available Currency market is recently the largest world market during the existence of which there have been many theories regarding the prediction of the development of exchange rates based on macroeconomic, microeconomic, statistic and other models. The aim of this paper is to identify the adequate model for the prediction of non-stationary time series of exchange rates and then use this model to predict the trend of the development of European currencies against Euro. The uniqueness of this paper is in the fact that there are many expert studies dealing with the prediction of the currency pairs rates of the American dollar with other currency but there is only a limited number of scientific studies concerned with the long-term prediction of European currencies with the help of the integrated ARMA models even though the development of exchange rates has a crucial impact on all levels of economy and its prediction is an important indicator for individual countries, banks, companies and businessmen as well as for investors. The results of this study confirm that to predict the conditional variance and then to estimate the future values of exchange rates, it is adequate to use the ARIMA (1,1,1 model without constant, or ARIMA [(1,7,1,(1,7] model, where in the long-term, the square root of the conditional variance inclines towards stable value.
DEVELOPMENT OF NEURO FUZZY CONTROLLER ALGORITHM FOR AIR CONDITIONING SYSTEM
AMRIT KAUR; ARSHDEEP KAUR
2012-01-01
The paper presents the neuro-fuzzy controller algorithm for air conditioning system. Neuro-fuzzy control combines the learning capabilities of neural networks and control capabilities of fuzzy logic control. The neurofuzzy controller for air conditioning system takes two inputs from temperature and humidity sensors and controls the compressor speed. The experimental results of the developed system are also shown.
DEVELOPMENT OF NEURO FUZZY CONTROLLER ALGORITHM FOR AIR CONDITIONING SYSTEM
AMRIT KAUR
2012-04-01
Full Text Available The paper presents the neuro-fuzzy controller algorithm for air conditioning system. Neuro-fuzzy control combines the learning capabilities of neural networks and control capabilities of fuzzy logic control. The neurofuzzy controller for air conditioning system takes two inputs from temperature and humidity sensors and controls the compressor speed. The experimental results of the developed system are also shown.
Adaptive Neuro-fuzzy approach in friction identification
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed
2016-06-01
In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.
CENTRIC MANAGEMENT SYSTEM BASED ON NEURO - FUZZY TOPOLOGY
Shumkov Y. A.
2014-11-01
Full Text Available The article describes the network-centric approach to a building control system based on the "inner teacher" neuro - fuzzy topology, which uses the principles of reinforcement learning
VLSI design of universal approximator neuro-fuzzy systems
Baturone, I.; Sánchez-Solano, Santiago; Barriga, Angel; Jiménez Fernández, Carlos Jesús; Senhadji, Raouf; D. R. López
2001-01-01
Neuro-fuzzy systems can theoretically solve any problem since they are universal approximators. Besides, they combine the advantages of the neuro and fuzzy paradigms. This paper describes and compares the different strategies that can be adopted to implement the learning and inference mechanisms involved in a neuro-fuzzy system. CAD tools, most of them integrated into the fuzzy system development environment Xfuzzy 2.0, have been developed to assist the designer in the implementation of ne...
PORTFOLIO OPTIMIZATION USING NEURO FUZZY SYSTEM IN INDIAN STOCK MARKET
Guna Sekar
2012-01-01
This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through diversification and right investment allocation to the particular stock under uncertainty. To evaluate the...
Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems
Rashmi Malhotra; D. K. Malhotra
2015-01-01
A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, ...
Design of a Neuro-Fuzzy Controller for Speed Control Applied to DC Servo Motor
Kim, S.H.; Kang, Y.H.; Kim, L.K. [Konkuk University, Seoul (Korea); Ko, B.W. [Cheju College of Technology, Cheju (Korea)
2002-02-01
In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules are created by an expert. To adapt the more precise model is implemented by error back-propagation learning algorithm to adjust the link-weight of fuzzy membership function in the neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of the proposed algorithm designed above, an operating characteristic of a DC servo motor with variable load is investigated. (author). 10 refs., 12 figs., 9 tabs.
Shahbudin, S.; Hussain, A.; El-Shafie, Ahmed; Tahir, N. M.; Samad, S. A.
In this paper, a neuro-fuzzy technique known as the Adaptive-Neuro Fuzzy Inference System (ANFIS) has been used to highlight the application of ANFIS to perform human posture classification task using the new simplified shock graph (SSG) representation. Basically, a shock graph is a shape abstraction that decomposed a shape into a set of hierarchically organized primitive parts. The shock graph that represents the silhouette of an object in terms of a set of qualitatively defined parts and organized in a hierarchical, directed acyclic graph is used as a powerful representation of human shape in our work. The SSG feature provides a compact, unique and simple way of representing human shape and has been tested with several classifiers. As such, in this paper we intend to test its efficacy with another classifier, that is, the ANFIS classifier system. The result showed that the proposed ANFIS model can be used in classifying various human postures.
Edge Detection with Neuro-Fuzzy Approach in Digital Synthesis Images
Fatma ZRIBI
2016-04-01
Full Text Available This paper presents an enhanced Neuro-Fuzzy (NF Approach of edge detection with an analysis of the characteristic of the method. The specificity of our method is an enhancement of the learning database of the diagonal edges compared to the original learning database. The original inspired NF edge detection model uses just one image learning database realized by Emin Yuksel. The tests are accomplished in synthesis images with a noised one of 20% of Gaussian noise.
Short Term Prediction of Highway Travel Time using Data Mining and Neuro-Fuzzy Methods
Coufal, David; Turunen, E.
Istanbul: Bogaziai University, 2003 - (Kaynak, O.), s. 175-182 ISBN 975-518-208-X. [IFSA 2003. International Fuzzy Systems Association World Congress /10./. Istanbul (TR), 30.06.2003-02.07.2003] R&D Projects: GA MŠk OC 274.001 Grant ostatní: COST(XE) Action 274 TARSKI Institutional research plan: AV0Z1030915 Keywords : data mining * neuro-fuzzy modelling Subject RIV: BA - General Mathematics
Gregor, Karol; Danihelka, Ivo; Mnih, Andriy; Blundell, Charles; Wierstra, Daan
2013-01-01
We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approximate parameter estimation method based on the minimum description length (MDL) principle, which can be seen as maximising a variational lower bound on the log-likelihood, with ...
Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.
2016-02-01
All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems
Rashmi Malhotra
2015-02-01
Full Text Available A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, case-based systems, or simply rules that have been developed through experience. Managers need a decision-making approach that is robust, competent, effective, efficient, and integrative to handle the multi-dimensional organizational entities. The decision maker deals with multiple players in an organization such as products, customers, competitors, location, geographic structure, scope, internal organization, and cultural dimension [46]. Sound decisions include two important concepts: efficiency (return on invested resources and effectiveness (reaching predetermined goals. However, quite frequently, the decision maker cannot simultaneously handle data from different sources. Hence, we recommend that managers analyze different aspects of data from multiple sources separately and integrate the results of the analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA and fuzzy logic. DEA evaluates and measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand fuzzy logic's main strength lies in handling imprecise data. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies. A major advantage of the DEA approach is that it clearly identifies the
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Mahdaoui, Rafik; Mouss, Mohamed Djamel; Chouhal, Ouahiba
2011-01-01
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures....
Adaptive neuro-fuzzy controller of switched reluctance motor
Tahour Ahmed
2007-01-01
Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.
Kepler AutoRegressive Planet Search
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
A neuro-fuzzy approach as medical diagnostic interface
Brause, Rüdiger W.; Friedrich, F.
2010-01-01
In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday 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-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and oth...
Neuro Fuzzy based Techniques for Predicting Stock Trends
Hemanth Kumar P.
2012-07-01
Full Text Available In this paper we discuss about Prediction of stock market returns.Artificial neural networks (ANNs have been popularly applied to finance problems such as stock exchange index prediction, bankruptcy prediction and corporate bond classification. An ANN model essentially mimics the learning capability of the human brain. A Fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. HereNeuro Fuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Methodologies are presented wherein neural predictors are used to anticipate the general behavior of financial indexes in the context of stocks and options trading. The methodologies are tested with actual financial data and shown considerable promise as a decision making and planning tool. In this paper methods are designed to predict 10-15 days of stock returns in advance.
Kepler AutoRegressive Planet Search: Motivation & Methodology
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal
Classification of EEG Signals by Radial Neuro-Fuzzy Systems
Coufal, David
2006-01-01
Roč. 5, č. 2 (2006), s. 415-423. ISSN 1109-2777 R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z10300504 Keywords : neuro-fuzzy systems * radial fuzzy systems * data mining * hybrid systems Subject RIV: BA - General Mathematics
First experience from in-core sensor validation based on correlation and neuro-fuzzy techniques
In this work new types of nuclear reactor in-core sensor validation methods are outlined. The first one is based on combination of correlation coefficients and mutual information indices, which reflect the correlation of signals in linear and nonlinear regions. The method may be supplemented by wavelet transform based signal features extraction and pattern recognition by artificial neural networks and also fuzzy logic based decision making. The second one is based on neuro-fuzzy modeling of residuals between experimental values and their theoretical counterparts obtained from the reactor core simulator calculations. The first experience with this approach is described and further improvements to enhance the outcome reliability are proposed (Author)
Predictive neuro-fuzzy controller for multilink robot manipulator
Kaymaz, Emre; Mitra, Sunanda
1995-10-01
A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems including multilink robot manipulators. The proposed controller is particularly useful when the dynamics of the nonlinear system to be controlled are difficult to yield exact solutions and the system specification can be obtained in terms of crisp input-output pairs. It inherits the advantages of both fuzzy logic and predictive control. The identification of the nonlinear mapping of the system to be controlled is realized by a three- layer feed-forward neural network model employing the input-output data obtained from the system. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as a simulation tool to generate the input-output data for developing the predictive fuzzy logic controller for the chosen nonlinear system. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the input and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, it is not necessary to tune the controller. For a two-link robot manipulator, the performance of this predictive fuzzy controller is shown to be superior to that of a conventional controller employing an ARMA model of the system in terms of accuracy and consumption of energy.
Adaptive neuro-fuzzy optimization of wind farm project net profit
Highlights: • Analyzing of wind farm project investment. • Net present value (NPV) maximization of the wind farm project. • Adaptive neuro-fuzzy (ANFIS) optimization of the number of wind turbines to maximize NPV. • The impact of the variation in the wind farm parameters. • Adaptive neuro fuzzy application. - Abstract: A wind power plant which consists of a group of wind turbines at a specific location is also known as wind farm. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. In this paper, in order to achieve the maximal net profit of a wind farm, an intelligent optimization scheme based on the adaptive neuro-fuzzy inference system (ANFIS) is applied. As the net profit measures, net present value (NPV) and interest rate of return (IRR) are used. The NPV and IRR are two of the most important criteria for project investment estimating. The general approach in determining the accept/reject/stay in different decision for a project via NPV and IRR is to treat the cash flows as known with certainty. However, even small deviations from the predetermined values may easily invalidate the decision. In the proposed model the ANFIS estimator adjusts the number of turbines installed in the wind farm, for operating at the highest net profit point. The performance of proposed optimizer is confirmed by simulation results. Some outstanding properties of this new estimator are online implementation capability, structural simplicity and its robustness against any changes in wind farm parameters. Based on the simulation results, the effectiveness of the proposed optimization strategy is verified
Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach.
Ang, Kai Keng; Quek, Chai
2006-09-01
This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model. PMID:17001989
Kang, Y.H.; Kim, L.K. [Konkuk University, Seoul (Korea)
2002-06-01
We designed a neuro-fuzzy controller to improve some problems that are happened when the DC servo motor is controlled by a PID controller or a fuzzy logic controller. Our model proposed in this paper has the stable and accurate responses, and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, the proposed controller is applied to the speed control of DC servo motor. The results showed that the proposed controller did not produce the overshoot, which happens when PID controller is used, and also it did not produce the steady state error when FLC is used. And also, it reduced the settling time about 10%. In addition, we could be aware that our model was only about 60% of the value of current peak of PID controller. (author). 10 refs., 9 figs., 5 tabs.
A Genetic Based Neuro-Fuzzy Controller System
Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications
Neuro-Fuzzy Support of Knowledge Management in Social Regulation
Petrovic-Lazarevic, Sonja; Coghill, Ken; Abraham, Ajith
2002-09-01
The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on social policies and procedures. Tacit knowledge is ethics and norms of the community. The former could be codified, stored and transferable in order to support decision making, while the latter being based on personal knowledge, experience and judgments is difficult to codify and store. Tacit knowledge expressed through linguistic information can be stored and used to support knowledge management in social regulation through the application of fuzzy and neuro-fuzzy logic.
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...
Seizure prediction using adaptive neuro-fuzzy inference system.
Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza
2013-01-01
In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. PMID:24110134
Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F
2014-10-15
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. PMID:24858196
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 (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.
Changho Jhin; Keum Taek Hwang
2015-01-01
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were...
DIAGNOSTICS IN HOMEOPATHIC SYSTEM USING NEURO-FUZZY NETWORKS
K.L. Kar; Rajiv Pathak
2012-01-01
The principle of Homeopathic system is to select a singlemedicine by priority of the symptoms not by the diseasesfor any patient. Neuro-Fuzzy logic networks can solves theproblem of the diagnostic in Homeopath System.Homeopathic software we suppose the neural networks forsolution the problem of the diagnostic in HomeopathSystem and consider the algorithms of the training. Neuralnetworks will adjust the wet value as symptoms. Usingintuitionistic fuzzy set theory medical diagnosis has beenappli...
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Rafik Mahdaoui; Mouss Leila-Hayet; Mohamed Djamel Mouss; Ouahiba Chouhal
2011-01-01
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing s...
SECURE ADHOC ROUTING FOR DATA TRANSFER USING NEURO FUZZY
Suganya; Nagarajan Srinivasan
2013-01-01
In the present world the security vulnerabilities are highly challenging in MANET. To get the maximum security and minimum threat there is lots of work going on. To effectively isolate the malicious node this paper proposes a Neuro fuzzy algorithm. By using fuzzy logic we can further improve the security level by identifying the malicious node more accurately. The concept behind the paper is as inreal life scenario, trust and sharing. Here in this paper we use the concept of trusting supporte...
Neuro-fuzzy controller to navigate an unmanned vehicle
Selma, Boumediene; Chouraqui, Samira
2013-01-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Contro...
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Rafik Mahdaoui
2011-05-01
Full Text Available Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.
Mohammad Subhi Al-batah
2014-01-01
Full Text Available 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.
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
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
Neuro-fuzzy pattern classification for fault diagnosis in nuclear components
The classification of patterns of measured quantities for diagnostic purposes is an important area of research with practical applications in a variety of fields ranging from industrial to medical. In particular, the classification of faults in nuclear components represents a fundamental task for the operation, control and accident management of nuclear power plants. In this paper, the fault diagnostic problem is tackled within a neuro-fuzzy approach to pattern classification. An important practical issue for the applicability of any diagnostic tool is the transparency of the underlying classification model, to allow for physical interpretation of the relationships between the underlying variables and for direct inspection for validation purposes. In this respect, the proposed neuro-fuzzy approach aims at obtaining not only a high rate of correct classification but also a transparent classification model, i.e., readable and easily interpretable from the physical point of view. For this reason, appropriate coverage and distinguishability constraints on the fuzzy input partitioning interface are introduced. The approach is tested on a diagnostic task concerning faults in the gland seals of the pumps of the primary heat transport system of the CANDU 6 nuclear reactor
Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density
UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID
Ali Moltajaei Farid
2013-01-01
Full Text Available ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capable of reasoning and learning in an uncertain and imprecise environment. In this paper, an adaptive neuro-fuzzy inference system (ANFIS is employed to control an unmanned aircraft vehicle (UAV. First, autopilots structure is defined, and then ANFIS controller is applied, to control UAVs lateral position. The results of ANFIS and PID lateral controllers are compared, where it shows the two controllers have similar results. ANFIS controller is capable to adaptation in nonlinear conditions, while PID has to be tuned to preserves proper control in some conditions. The simulation results generated by Matlab using Aerosim Aeronautical Simulation Block Set, which provides a complete set of tools for development of six degree-of-freedom. Nonlinear Aerosonde unmanned aerial vehicle model with ANFIS controller is simulated to verify the capability of the system. Moreover, the results are validated by FlightGear flight simulator.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata
2014-12-01
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root
APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN INTEREST RATES EFFECTS ON STOCK RETURNS
ELEFTHERIOS GIOVANIS
2011-02-01
Full Text Available In the current study we examine the effects of interest rate changes on common stock returns of Greek banking sector. We examine theGeneralized Autoregressive Heteroskedasticity (GARCH process and an Adaptive Neuro-Fuzzy Inference System (ANFIS. The conclusions of our findings are that the changes of interest rates, based on GARCH model, are insignificant on common stock returns during the period we examine. On the other hand, with ANFIS we can get the rules and in each case we can have positive or negative effects depending on the conditions and the firing rules of inputs, which information is not possible to be retrieved with the traditional econometric modelling. Furthermore we examine the forecasting performance of both models and we conclude that ANFIS outperforms GARCH model in both in-sample and out-of-sample periods.
Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems
Shieh, M-Y; Chang, K-H [Department of E. E., Southern Taiwan University, 1 Nantai St., YungKang City, Tainan County 71005, Taiwan (China); Lia, Y-S [Executive Director Office, ITRI, Southern Taiwan Innovation Park, Tainan County, Taiwan (China)], E-mail: myshieh@mail.stut.edu.tw
2008-02-15
This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.
Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems
This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Ramazan Havangi
2010-03-01
Full Text Available 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 much possible. The simulation results show the effectiveness of the proposed algorithm.
A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors
Evelio José González
2009-12-01
Full Text Available 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.
Design of uav robust autopilot based on adaptive neuro-fuzzy inference system
Mohand Achour Touat
2008-04-01
Full Text Available This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.
Prediction of contact forces of underactuated finger by adaptive neuro fuzzy approach
Petković, Dalibor; Shamshirband, Shahaboddin; Abbasi, Almas; Kiani, Kourosh; Al-Shammari, Eiman Tamah
2015-12-01
To obtain adaptive finger passive underactuation can be used. Underactuation principle can be used to adapt shapes of the fingers for grasping objects. The fingers with underactuation do not require control algorithm. In this study a kinetostatic model of the underactuated finger mechanism was analyzed. The underactuation is achieved by adding the compliance in every finger joint. Since the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. In this study prediction of the contact forces was established by a soft computing approach. Adaptive neuro-fuzzy inference system (ANFIS) was applied as the soft computing method to perform the prediction of the finger contact forces.
Adaptive Neuro-fuzzy Inference System as Cache Memory Replacement Policy
CHUNG, Y. M.
2014-02-01
Full Text Available To date, no cache memory replacement policy that can perform efficiently for all types of workloads is yet available. Replacement policies used in level 1 cache memory may not be suitable in level 2. In this study, we focused on developing an adaptive neuro-fuzzy inference system (ANFIS as a replacement policy for improving level 2 cache performance in terms of miss ratio. The recency and frequency of referenced blocks were used as input data for ANFIS to make decisions on replacement. MATLAB was employed as a training tool to obtain the trained ANFIS model. The trained ANFIS model was implemented on SimpleScalar. Simulations on SimpleScalar showed that the miss ratio improved by as high as 99.95419% and 99.95419% for instruction level 2 cache, and up to 98.04699% and 98.03467% for data level 2 cache compared with least recently used and least frequently used, respectively.
A neuro-fuzzy controller for xenon spatial oscillations in load-following operation
Na, Man Gyun [Chosun University, Kwangju (Korea, Republic of); Upadhyaya, Belle R. [The University of Tennessee, Knoxville (United States)
1997-12-31
A neuro-fuzzy control algorithm is applied for xenon spatial oscillations in a pressurized water reactor. The consequent and antecedent parameters of the fuzzy rules are tuned by the gradient descent method. The reactor model used for computer simulations is a two-point xenon oscillation model. The reactor core is axially divided into two regions and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control method exhibits very responses to a step or a ramp change of target axial offest without any residual flux oscillations. 9 refs., 5 figs. (Author)
NURWAHA Deogratias; WANG Xin-hou
2008-01-01
This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference (ANFIS) and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yarn count are used as inputs to train the two models and the count-strength-product (CSP) was the target. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multipleregression model. The impact of each fiber property is also illustrated.
New concept of direct torque neuro-fuzzy control for induction motor drives. Simulation study
Grabowski, P.Z. [Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw (Poland)
1997-12-31
This paper presents a new control strategy in the discrete Direct Torque Control (DTC) based on neuro-fuzzy structure. Two schemes are proposed: neuro-fuzzy switching times calculator and neuro-fuzzy incremental controller with space vector modulator. These control strategies guarantee very good dynamic and steady-states characteristics, with very low sampling time and constant switching frequency. The proposed techniques are verified by simulation study of the whole drive system and results are compared with conventional discrete Direct Torque Control method. (orig.) 18 refs.
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.
Neuro fuzzy force control for soft dry contact Hertzian ultrasonic probe
Gallegos, E.; Baltazar, A.; Treesatayapun, C.
2016-02-01
In this work the use of a cartesian robotic manipulator as scanner for the automated identification of hidden defects in an aluminum test plate is proposed. The robotic manipulator includes a custom made soft deformable ultrasonic probe and a force sensor for the recollection of the ultrasonic signals and force feedback. The contact between the soft probe and the test plate is regulated using a Neuro Fuzzy controller in order to avoid the complex mathematical model produced by the interaction. Finally the use of the correlation coefficient is proposed for the post processing of the obtained ultrasonic signals and identification of hidden defects inside the test plate. Experimental studies demonstrated the efficiency of the method.
Vaganova, E. V.; Syryamkin, M. V.
2015-11-01
The purpose of the research is the development of evolutionary algorithms for assessments of promising scientific directions. The main attention of the present study is paid to the evaluation of the foresight possibilities for identification of technological peaks and emerging technologies in professional medical equipment engineering in Russia and worldwide on the basis of intellectual property items and neural network modeling. An automated information system consisting of modules implementing various classification methods for accuracy of the forecast improvement and the algorithm of construction of neuro-fuzzy decision tree have been developed. According to the study result, modern trends in this field will focus on personalized smart devices, telemedicine, bio monitoring, «e-Health» and «m-Health» technologies.
This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles
Chau, K.T.; Wu, K.C.; Chan, C.C. [University of Hong Kong (China). Dept. of Electrical and Electronic Engineering
2004-07-01
This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles. (author)
Otilia Elena Dragomir
2015-11-01
Full Text Available The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1 and the shape of membership functions (Scenario 2.
Position control of ionic polymer metal composite actuator based on neuro-fuzzy system
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.
DIAGNOSTICS IN HOMEOPATHIC SYSTEM USING NEURO-FUZZY NETWORKS
K.L. Kar
2012-06-01
Full Text Available The principle of Homeopathic system is to select a singlemedicine by priority of the symptoms not by the diseasesfor any patient. Neuro-Fuzzy logic networks can solves theproblem of the diagnostic in Homeopath System.Homeopathic software we suppose the neural networks forsolution the problem of the diagnostic in HomeopathSystem and consider the algorithms of the training. Neuralnetworks will adjust the wet value as symptoms. Usingintuitionistic fuzzy set theory medical diagnosis has beenapplied to the problem of selection of single remedy fromhomeopathic repertorization. Two types of compositions ofIFRs and three types of selection indices have beendiscussed. We also propose a new repertory exploiting thebenefits of sof-intelligence.
Adaptive neuro-fuzzy inference system based automatic generation control
Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)
2008-07-15
Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)
KAMPOUROPOULOS, K.
2014-02-01
Full Text Available This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithms (GA. The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.
Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization
Boumediene ALLAOUA; Abdellah LAOUFI; Gasbaoui, Brahim; Abdessalam ABDERRAHMANI
2009-01-01
This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS) control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally,...
Neuro-fuzzy soft-switching hybrid filter for impulsive noisy environments
ÖZER, Şaban; ZORLU, Hasan
2011-01-01
In this study, a new soft-switching hybrid filter based on a neuro-fuzzy network for impulsive noisy environments is proposed. The hybrid filter was built by combining an adaptive finite impulse response (FIR) filter, an adaptive weighted myriad (WMy) filter, and a soft-switching mechanism based on a neuro-fuzzy (NF) network. Performance of the hybrid filter was tested in a-stable noisy situations and compared with adaptive FIR, WMy, and weighted median (WMd) filter performances. Acc...
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
P. Akhavan
2014-10-01
Full Text Available 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.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
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.
Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques
Banadaki Hamed Dehghan
2015-01-01
Full Text Available The walking beam furnace (WBF is one of the most prominent process plants often met in an alloy steel production factory and characterized by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real WBF using non-linear black-box sub-system identification based on locally linear neuro-fuzzy (LLNF model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i.e., ninety seconds ahead, developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree (LOLIMOT which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE solution proves that all operating zones of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the WBF process.
Torshabi, Ahmad Esmaili
2014-12-01
In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works. PMID:25412886
A DEOXYRIBONUCLEIC ACID COMPRESSION ALGORITHM USING AUTO-REGRESSION AND SWARM INTELLIGENCE
Walid Aly
2013-01-01
Full Text Available DNA compression challenge has become a major task for many researchers as a result of exponential increase of produced DNA sequences in gene databases; in this research we attempt to solve the DNA compression challenge by developing a lossless compression algorithm. The proposed algorithm works in horizontal mode using a substitutional-statistical technique which is based on Auto Regression modeling (AR, the model parameters are determined using Particle Swarm Optimization (PSO. This algorithm is called Swarm Auto-Regression DNA Compression (SARDNAComp. SARDNAComp aims to reach higher compression ratio which make its application beneficial for both practical and functional aspects due to reduction of storage, retrieval, transmission costs and inferring structure and function of sequences from compression, SARDNAComp is tested on eleven benchmark DNA sequences and compared to current algorithms of DNA compression, the results showed that (SARDNAComp outperform these algorithms.
Kepler AutoRegressive Planet Search: Initial Results
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The statistical analysis procedures of the Kepler AutoRegressive Planet Search (KARPS) project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve, but visual inspection of the residual series shows that significant deviations from Gaussianity remain for many of them. Although the reduction in stellar signal is encouraging, it is important to note that the transit signal is also altered in the resulting residual time series. The periodogram derived from our Transit Comb Filter (TCF) is most effective for shorter period planets with quick ingress/egress times (relative to Kepler's 29-minute sample rate). We do not expect high sensitivity to periods of hundreds of days. Our findings to date on real-data tests of the KARPS methodology will be discussed including confirmation of some Kepler Team `candidate' planets, no confirmation of some `candidate' and `false positive' sytems, and suggestions of mischosen harmonics in the Kepler Team periodograms. We also present cases of new possible planetary signals.
Tarkan Erdik; Zekai Şen
2008-12-01
Singh et al (2005)examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However,the model proposed by them has some drawbacks according to fuzzy logic principles.This discussion will focus on the main fuzzy logic principles which authors and potential readers should take into consideration.
SECURE ADHOC ROUTING FOR DATA TRANSFER USING NEURO FUZZY
Suganya
2013-04-01
Full Text Available In the present world the security vulnerabilities are highly challenging in MANET. To get the maximum security and minimum threat there is lots of work going on. To effectively isolate the malicious node this paper proposes a Neuro fuzzy algorithm. By using fuzzy logic we can further improve the security level by identifying the malicious node more accurately. The concept behind the paper is as inreal life scenario, trust and sharing. Here in this paper we use the concept of trusting supporters, sharing the companion list and routing through data. In order to get a secure high trust level, fuzzy logic is applied for evaluating routing response and isolates the malicious node. Trusted route is evaluated in sequence of operation and data is transferred at a most trusted level. Trust values are computed to each node by setting verge values. The values of each node is checked with the verge value. If the value higherthan the verge value mark it as high trusted node or else low trusted node.The fuzzy logic is implemented using aarmp routing protocol. Thus the level of trust is increased to obtain accuracy of identification. The goal of getting a robust route without any malicious node is achieved.
An efficient Neuro-Fuzzy approach to nuclear power plant transient identification
Gomes da Costa, Rafael [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Abreu Mol, Antonio Carlos de, E-mail: mol@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Instituto Nacional de C and T de Reatores Nucleares Inovadores (Brazil); Carvalho, Paulo Victor R. de, E-mail: paulov@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Lapa, Celso Marcelo Franklin, E-mail: lapa@ien.gov.br [Instituto de Engenharia Nuclear - CNEN, Programa de Pos-Graduacao em Ciencia e Tecnologia Nucleares, Via Cinco, s/no, Cidade Universitaria, Rua Helio de Almeida, 75, Postal Box 68550, Zip Code 21941-906 Rio de Janeiro (Brazil); Instituto Nacional de C and T de Reatores Nucleares Inovadores (Brazil)
2011-06-15
Highlights: > We investigate a Neuro-Fuzzy modeling tool use for able transient identification. > The prelusive transient type identification is done by an artificial neural network. > After, the fuzzy-logic system analyzes the results emitting reliability degree of it. > The research support was made in a PWR simulator at the Brazilian Nuclear Engineering Institute. > The results show the potential to help operators' decisions in a nuclear power plant. - Abstract: Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the 'don't know' as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute
An efficient Neuro-Fuzzy approach to nuclear power plant transient identification
Highlights: → We investigate a Neuro-Fuzzy modeling tool use for able transient identification. → The prelusive transient type identification is done by an artificial neural network. → After, the fuzzy-logic system analyzes the results emitting reliability degree of it. → The research support was made in a PWR simulator at the Brazilian Nuclear Engineering Institute. → The results show the potential to help operators' decisions in a nuclear power plant. - Abstract: Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the 'don't know' as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute (IEN
Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system
Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)
2008-07-01
This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823
Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems
Samira Chabaa
2009-01-01
Full Text Available Problem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been reported by several researchers. The goal of the present contribution was to complement these previous researches by predicting network traffic data. Approach: The Adaptive Neuro-Fuzzy Inference System (ANFIS was realized by an appropriate combination of fuzzy systems and neural networks. It was applied in different applications which have been increased in recent years and have multidisciplinary in several domains with a high accuracy. For this reason, we used a set of input and output data of packet transmission over Internet Protocol (IP networks as input and output of ANFIS to develop a model for predicting data. Results: ANFIS was compared with some existing model based on Volterra system with Laguerre functions. The obtained results demonstrate that the sequences of generated values have the same statistical characteristics as those really observed. Furthermore, the relative error using ANFIS model was better than this obtained by Volterra system model. Conclusion: The developed model fits well real data and can be used for predicting purpose with a high accuracy.
A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance
Quang Hung Do
2013-01-01
Full Text Available Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.
Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers
Kim, Sung-Woo; Park, Sang-Young; Park, Chandeok
2016-01-01
In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.
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.
Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.
Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L
2012-01-01
Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665
Intelligent Control for Self-erecting Inverted Pendulum Via Adaptive Neuro-fuzzy Inference System
A. A. Saifizul
2006-01-01
Full Text Available A self-erecting single inverted pendulum (SESIP is one of typical nonlinear systems. The control scheme running the SESIP consists of two main control loops. Namely, these control loops are swing-up controller and stabilization controller. A swing-up controller of an inverted pendulum system must actuate the pendulum from the stable position. While a stabilization controller must stand the pendulum in the unstable position. To deal with this system, a lot of control techniques have been used on the basis of linearized or nonlinear model. In real-time implementation, a real inverted pendulum system has state constraints and limited amplitude of input. These problems make it difficult to design a swing-up and a stabilization controller. In this paper, first, the mathematical models of cart and single inverted pendulum system are presented. Then, the Position-Velocity controller is designed to swing-up the pendulum considering physical behavior. For stabilizing the inverted pendulum, a Takagi-Sugeno fuzzy controller with Adaptive Neuro-Fuzzy Inference System (ANFIS architecture is used to guarantee stability at unstable equilibrium position. Experimental results are given to show the effectiveness of these controllers.
Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)
2008-06-15
Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)
Estimation of Loose Status of Jigging Bed Based on Adaptive Neuro-Fuzzy Inference System
CHENG Jian; GUO Yi-nan; QIAN Jian-sheng
2006-01-01
In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line directly in industrial process situations. So a soft-sensor technology is needed for this purpose. The soft-sensor model is developed in the experiment by an adaptive neuro-fuzzy inference system (ANFIS) which has a remarkable ability of learning and generalization. Based on the analysis of the technologic mechanism of jigging bed, the structure of the ANFIS is established to build the soft-sensor model of loose status estimation. The ANFIS is trained by a hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, which indicate that the ANFIS has better abilities of learning and generalization than the RBF and the BP networks. Thus, it is possible that the loose status of the jigging bed can be estimated on-line by using ANFIS.
Hybrid neuro-fuzzy system for power generation control with environmental constraints
The real time controls at the central energy management centre in a power system, continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is least. However due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained combined economic dispatch formulation is to estimate the optimal generation allocation to generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques are cumbersome for such complex optimization tasks and are not suitable for on-line use due to increased computational burden. This paper proposes a neuro-fuzzy power dispatch method where the uncertainty involved with power demand is modeled as a fuzzy variable. Then Levenberg-Marquardt neural network (LMNN) is used to evaluate the optimal generation schedules. This model trains almost hundred times faster that the popular BP neural network. The proposed method has been tested on two test systems and found to be suitable for on-line combined environmental economic dispatch
Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)
The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude
Weiji Wang
2010-10-01
Full Text Available A Field Programmable Gate Array (FPGA is proposed to build an Adaptive Neuro Fuzzy Inference System(ANFIS for controlling a full vehicle nonlinear active suspension system. A Very High speed integratedcircuit Hardware Description Language (VHDL has been used to implement the proposed controller. Anoptimal Fraction Order PIlDμ (FOPID controller is designed for a full vehicle nonlinear activesuspension system. Evolutionary Algorithm (EA has been applied to modify the five parameters of theFOPID controller (i.e. proportional constant Kp, integral constant Ki, derivative constant Kd, integralorder l and derivative order μ. The data obtained from the FOPID controller are used as a reference todesign the ANFIS model as a controller for the controlled system. A hybrid approach is introduced to trainthe ANFIS. A Matlab Program has been used to design and simulate the proposed controller. The ANFIScontrol parameters obtained from the Matlab program are used to write the VHDL codes. Hardwareimplementation of the FPGA is dependent on the configuration file obtained from the VHDL program. Theexperimental results have proved the efficiency and robustness of the hardware implementation for theproposed controller. It provides a novel technique to be used to design NF controller for full vehiclenonlinear active suspension systems with hydraulic actuators.
Ammar A. Aldair
2010-10-01
Full Text Available A Field Programmable Gate Array (FPGA is proposed to build an Adaptive Neuro Fuzzy Inference System (ANFIS for controlling a full vehicle nonlinear active suspension system. A Very High speed integrated circuit Hardware Description Language (VHDL has been used to implement the proposed controller. An optimal Fraction Order PIλ D µ (FOPID controller is designed for a full vehicle nonlinear active suspension system. Evolutionary Algorithm (EA has been applied to modify the five parameters of the FOPID controller (i.e. proportional constant Kp, integral constant Ki , derivative constant Kd, integral order λ and derivative order µ. The data obtained from the FOPID controller are used as a reference to design the ANFIS model as a controller for the controlled system. A hybrid approach is introduced to train the ANFIS. A Matlab Program has been used to design and simulate the proposed controller. The ANFIS control parameters obtained from the Matlab program are used to write the VHDL codes. Hardware implementation of the FPGA is dependent on the configuration file obtained from the VHDL program. The experimental results have proved the efficiency and robustness of the hardware implementation for the proposed controller. It provides a novel technique to be used to design NF controller for full vehicle nonlinear active suspension systems with hydraulic actuators.
Applying a neuro-fuzzy approach for transient identification in a nuclear power plant
Costa, Rafael G.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.; Carvalho, Paulo V.R., E-mail: rgcosta@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: paulov@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2009-07-01
Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Several systems based on specialist systems, neural networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant. (author)
Applying a neuro-fuzzy approach for transient identification in a nuclear power plant
Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Several systems based on specialist systems, neural networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant. (author)
Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)
Kakar, Manish [Department of Radiation Biology, Norwegian Radium Hospital, Montebello, 0310 Oslo (Norway); Nystroem, Haakan [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Aarup, Lasse Rye [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Noettrup, Trine Jakobi [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Olsen, Dag Rune [Department of Radiation Biology, Norwegian Radium Hospital, Montebello, 0310 Oslo (Norway); Department of Medical Physics and Technology, Norwegian Radium Hospital, Oslo (Norway); Department of Physics, University of Oslo (Norway)
2005-10-07
The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.
Development of Neuro-fuzzy System for Early Prediction of Heart Attack
Obanijesu Opeyemi
2012-08-01
Full Text Available This work is aimed at providing a neuro-fuzzy system for heart attack detection. Theneuro-fuzzy system was designed with eight input field and one output field. The input variables are heart rate, exercise, blood pressure, age, cholesterol, chest pain type, blood sugar and sex. The output detects the risk levels of patients which are classified into 4 different fields: very low, low, high and very high. The data set used was extracted from the database and modeled in order to make it appropriate for the training, then the initial FIS structure was generated, the network was trained with the set of training data after which it was tested and validated with the set of testing data. The output of the system was designed in a way that the patient can use it personally. The patient just need to supply some values which serve as input to the system and based on the values supplied the system will be able to predict the risk level of the patient.
FPGA Implementation of Adaptive Neuro-Fuzzy Inference Systems Controller for Greenhouse Climate
Charaf eddine LACHOURI
2016-01-01
Full Text Available This paper describes a Field-programmable Gate Array (FPGA implementation of Adaptive Neuro-fuzzy Inferences Systems (ANFIS using Very High-Speed Integrated Circuit Hardware-Description Language (VHDL for controlling temperature and humidity inside a tomato greenhouse. The main advantages of using the HDL approach are rapid prototyping and allowing usage of powerful synthesis controller through the use of the VHDL code. The use of hardware description language (HDL in the application is suitable for implementation into an Application Specific Integrated Circuit (ASIC and Field tools such as Quartus II 8.1. A set of six inputs meteorological and control actuators parameters that have a major impact on the greenhouse climate was chosen to represent the growing process of tomato plants. In this contribution, we discussed the construction of an ANFIS system that seeks to provide a linguistic model for the estimation of greenhouse climate from the meteorological data and control actuators during 48 days of seedlings growth embedded in the trained neural network and optimized using the backpropagation and the least square algorithm with 500 iterations. The simulation results have shown the efficiency of the implemented controller.
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
Aderemi A Atayero
2012-04-01
Full Text Available ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index are used to adjust hysteresis task of load balancing.
Statistical early-warning indicators based on Auto-Regressive Moving-Average processes
Faranda, Davide; Dubrulle, Bérengère
2014-01-01
We address the problem of defining early warning indicators of critical transition. To this purpose, we fit the relevant time series through a class of linear models, known as Auto-Regressive Moving-Average (ARMA(p,q)) models. We define two indicators representing the total order and the total persistence of the process, linked, respectively, to the shape and to the characteristic decay time of the autocorrelation function of the process. We successfully test the method to detect transitions in a Langevin model and a 2D Ising model with nearest-neighbour interaction. We then apply the method to complex systems, namely for dynamo thresholds and financial crisis detection.
Al-Hinti, I.; Sakhrieh, A. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan); Samhouri, M.; Al-Ghandoor, A. [Department of Industrial Engineering, The Hashemite University, Zarqa 13115 (Jordan)
2009-01-15
This paper uses a neuro-fuzzy interface system (ANFIS) to study the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP), and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine. Experimental data were used as inputs to ANFIS to simulate the engine performance characteristics. The experimental as well as the model results emphasize the role of boost pressure in improving the different engine characteristics. The results show that the ANFIS technique can be used adequately to identify the effect of boost pressure on the different engine characteristics. In addition, different data points that were not used for ANFIS training were used to validate the developed models. The results suggest that ANFIS can be used accurately to predict the effect of boost pressure on the different engine characteristics. (author)
A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass
T N Singh; R Kanchan; A K Verma; K Saigal
2005-02-01
Physico-mechanical properties of rocks have great significance in all operational parts in mining activities, from exploration to final dispatch of material. Compressional wave velocity (-wave velocity) and anisotropic behaviour of rocks are two such properties which help to understand the rock response under varying stress conditions. They also influence the breakage mechanism of rock. There are different methods to determine the -wave velocity and anisotropy in situ and in the laboratory. These methods are cumbersome and time consuming. Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed and compared two di®erent models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial neural network systems, for the prediction of compressional wave velocity.
FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
Levent Akin
2008-11-01
Full Text Available Finding optimal solutions to Partially Observable Markov Decision Problems is known to be NP-hard. This paper describes a novel neuro-fuzzy approach to obtain fast, robust and easily interpreted solutions by utilizing a combination of several learning techniques including neural networks, fuzzy decision making and Q-learning.
FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems
Levent Akin; Toygar Karadeniz
2004-01-01
Finding optimal solutions to Partially Observable Markov Decision Problems is known to be NP-hard. This paper describes a novel neuro-fuzzy approach to obtain fast, robust and easily interpreted solutions by utilizing a combination of several learning techniques including neural networks, fuzzy decision making and Q-learning.
Al-Shammari, Eiman Tamah; Petković, Dalibor; Danesh, Amir Seyed; Shamshirband, Shahaboddin; Issa, Mirna; Zentner, Lena
2016-05-01
Robotic operations need to be safe for unpredictable contacts. Joints with passive compliance with springs can be used for soft robotic contacts. However the joints cannot measure external collision forces. In this investigation was developed one passive compliant joint which have soft contacts with external objects and measurement capabilities. To ensure it, conductive silicone rubber was used as material for modeling of the compliant segments of the robotic joint. These compliant segments represent embedded sensors. The conductive silicone rubber is electrically conductive by deformations. The main task was to obtain elastic absorbers for the external collision forces. These absorbers can be used for measurement in the same time. In other words, the joint has an internal measurement system. Adaptive neuro fuzzy inference system (ANFIS) was used to estimate the safety level of the robotic joint by head injury criteria (HIC).
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. PMID:25453384
Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
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.
Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng
2016-05-01
Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.
Neuro-fuzzy Logic Control of Single Phase Matrix Converter Fed Induction Heating System
P. Umasankar
2015-02-01
Full Text Available This study presents a design and simulation of Neuro-Fuzzy Logic Controlled (NFLC Single Phase Matrix Converter (SPMC fed Induction Heating (IH system. Single phase matrix converter system is an AC-AC converter which eliminates the usage of reactive storage elements and its performance over varying operating frequencies can be controlled by varying the Pulse Width Modulation (PWM signal fed to the switches of single phase matrix converter. In the existing system a Fuzzy Logic Controller (FLC was designed to control the matrix converter which yielded low Total Harmonic Distortion (THD values when compared to previous systems. In this study a Neuro-Fuzzy Logic Controller was designed to control the single phase matrix converter and the results obtained prove its advantage over the existing Fuzzy Logic based control system.
Stabilization of an inverted pendulum system via an SIRM neuro-fuzzy controller
Kulworawanichpong, T.
2005-01-01
Full Text Available This article presents a new neuro-fuzzy controller to stabilize an inverted pendulum system. The proposed controller consists of the Single Input Rule Modules (SIRMs, the artificial neural network (ANN and the dynamic importance degrees (DIDs. It simultaneously controls both the angle of the pendulum and the position of the cart. The learning of the ANN results in the DIDs. The proposed controller has a simple structure that can decrease the number of fuzzy rules. The simulation results show that the proposed neurofuzzy controller has an ability to stabilize a wide range of the inverted pendulum system within a short periodof time. Moreover, the comparisons of the simulation results between the proposed neuro-fuzzy controller and the SIRMs fuzzy controller are revealed in this article.
Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization
Boumediene ALLAOUA
2009-12-01
Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.
Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications
Rao, D. Srinivasa; Seetha, M; Prasad, M. H. M. Krishna
2012-01-01
Image fusion is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Image fusion process is required for different applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is required. In this paper, image fusion using fuzzy and neuro fuzzy lo...
CLASSIFICATION OF EEG SIGNAL DATA USING HYBRID OF NEURO-FUZZY
Pratibha Rana*, Ms. Jyotsna Singh
2016-01-01
Brain Computing interface technology represents a very highly growing field now-a-days for the research because of its unique applications system. In this paper we investigate classification methods of mental commands based on EEG data for BCI. The aim of this study is to present the work of training an artificial neural network (ANN) and Neuro-Fuzzy system provided with the data of a few healthy people who are in different mental states thinking about different activities like eating, walkin...
Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-Fuzzy Compensation
Henriques, L.; Rolim, L.; Suemitsu, W.; Branco, P. J. Costa; Dente, J. A.
2000-01-01
Simple power electronic drive circuit and fault tolerance of converter are specific advantages of SRM drives, but excessive torque ripple has limited its use to special applications. It is well known that controlling the current shape adequately can minimize the torque ripple. This paper presents a new method for shaping the motor currents to minimize the torque ripple, using a neuro-fuzzy compensator. In the proposed method, a compensating signal is added to the output of a PI controller, in...
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Tran, Tung; Yang, Bo-Suk; Oh, Myung-Suck; Tan, Andy Chit Chiow
2009-01-01
This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and le...
A Neuro-Fuzzy Approach to Classification of ECG Signals for Ischemic Heart Disease Diagnosis
Neagoe, Victor-Emil; Iatan, Iuliana-Florentina; Grunwald, Sorin
2003-01-01
The paper focuses on the neuro-fuzzy classifier called Fuzzy-Gaussian Neural Network (FGNN) to recognize the ECG signals for Ischemic Heart Disease (IHD) diagnosis. The proposed ECG processing cascade has two main stages: (a) Feature extraction from the QRST zone of ECG signals using either the Principal Component Analysis (PCA) or the Discrete Cosine Transform (DCT); (b) Pattern classification for IHD diagnosis using the FGNN. We have performed the software implementation and have experiment...
Location Estimation and Mobility Prediction Using Neuro-fuzzy Networks In Cellular Networks
Maryam Borna; Mohammad Soleimani
2011-01-01
In this paper an approach is proposed for location estimation, tracking and mobility prediction in cellular networks in dense urban areas using neural and neuro-fuzzy networks. In urban areas with high buildings, due to the effects of multipath fading and Non-Line-of-Sight conditions, the accuracy of positioning methods based on direction finding and ranging degrades significantly. Also in these areas, due to high user traffic there's a need for network resources management. Knowing the next ...
Application of Adaptive Neuro-Fuzzy Inference System for Information Secuirty
Sureswaran Ramadass
2012-01-01
Full Text Available Problem statement: Computer networks are expanding at very fast rate and the number of network users is increasing day by day, for full utilization of networks it need to be secured against many threats including malware, which is harmful software with the capability to damage data and systems. Fuzzy rule based classification systems considered as an active research area in recent years, due to their unique capability of classifying. Approach: This study presents a neural fuzzy classifier based on Adaptive Neuro-Fuzzy Inference System (ANFIS for malware detection. Firstly, the malware exe files was analyzed and the most important API calls were selected and used as training and testing datasets, using the training data set the ANFIS classifier learned how to detect the malware in the test dataset. Results and Conclusion: The performances of the Neuro fuzzy classifier were evaluated based on the performance of training and accuracy of classification, the results show that the proposed Neuro fuzzy classifier can detect the malware exe files effectively.
Ravi Samikannu
2011-01-01
Full Text Available Problem statement: The temperature control in plastic extrusion machine is an important factor to produce high quality plastic products. The first order temperature control system in plastic extrusion comprises of coupling effects, long delay time and large time constants. Controlling temperature is very difficult as the process is multistage process and the system coupled with each other. In order to conquer this problem the system is premeditated with neuro fuzzy controller using LabVIEW. Approach: The existing technique involved is conventional PID controller, Neural controller, mamdani type Fuzzy Logic Controller and the proposed method is neuro fuzzy controller. Results: Manifest feature of the proposed method is smoothing of undesired control signal of conventional PID, neural controller and mamdani type FLC controller. The software incorporated the LabVIEW graphical programming language and MATLAB toolbox were used to design temperature control in plastic extrusion system. Hence neuro fuzzy controller is most powerful approach to retrieve the adaptiveness in the case of nonlinear system. Conclusion: The tuning of the controller was synchronized with the controlled variable and allowing the process at its desired operating condition. The results indicated that the use of proposed controller improve the process in terms of time domain specification, set point tracking and also reject disturbances with optimum stability.
Oliveira, Mauro Vitor de
1999-06-15
This work develops two models of signal validation in which the analytical redundancy of the monitored signals from an industrial plant is made by neural networks. In one model the analytical redundancy is made by only one neural network while in the other it is done by several neural networks, each one working in a specific part of the entire operation region of the plant. Four cluster techniques were tested to separate the entire region of operation in several specific regions. An additional information of systems' reliability is supplied by a fuzzy inference system. The models were implemented in C language and tested with signals acquired from Angra I nuclear power plant, from its start to 100% of power. (author)
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
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.
Heidary, Saeed, E-mail: saeedheidary@aut.ac.ir; Setayeshi, Saeed, E-mail: setayesh@aut.ac.ir
2015-01-11
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 {sup 99m}Tc/{sup 201}Tl 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 {sup 201}Tl (77±10% keV) and {sup 99m}Tc (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.
Elizabeth Catalina Zapata Gómez
2008-12-01
utiliza-se comummente como referente na literatura de séries de tempo. Os resultados indicam que o modelo desenvolvido representa melhor que outros modelos de características similares a dinâmica da série estudada.This paper proposes a new kind of non-linear hybrid model. In the proposed model, mean non-linearity is represented by using an adaptive neuro-fuzzy inference system (ANFIS whereas variance is represented using a conditional self-regressive heteroscedastic component. The mathematical formula for this type of model is shown and a method to estimate it is proposed. In addition, a specification strategy is developed for the proposed model, based on a battery of statistical soft transaction regression (STR tests and on verosimility radius testing. As a case study, the IBM stock closing price series dynamics were modeled, which is commonly used as a benchmark in the literature on time series. Results indicate that the model developed represents the dynamics of the studied series better than other models with similar characteristics.
Highlights: ► An ANFIS model is developed for predicting sour gas hydrate dissociation conditions. ► It can be used over wide ranges of operating conditions. ► At all H2S concentrations, the developed model outperforms the thermodynamic models. ► The presented model is useful for design of industrial sour gas handling systems. - Abstract: An adaptive neuro fuzzy inference system (ANFIS) has been proposed for predicting the sour gas hydrate equilibrium dissociation conditions. The proposed model predictions have been compared with those of the available thermodynamic models at different operating conditions. It is found that at all H2S concentrations especially at the concentrations higher than 10 mol%, the developed ANFIS model outperforms the existing thermodynamic models with the average absolute deviation of 2.18%. The proposed ANFIS model can be used for accurate and reliable predictions of sour gas hydrate equilibrium conditions over wide ranges of temperatures and acid gas concentrations and is a useful tool for proper design of sour natural gas flow assurance systems and gas hydrate energy storage processes in oil and gas industries.
Jhin, Changho; Hwang, Keum Taek
2014-01-01
Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627
Jhin, Changho; Hwang, Keum Taek
2015-01-01
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167
Changho Jhin
Full Text Available One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS applied quantitative structure-activity relationship models (QSAR were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.
Paulchamy Balaiah
2012-01-01
Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors