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Speed Neuro-fuzzy Estimator Applied To Sensorless Induction Motor Control

Lima, F.; Kaiser, Walter; Silva, Ivan Nunes da; Oliveira Junior, Azauri Albano de
Fonte: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC; PISCATAWAY Publicador: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC; PISCATAWAY
Tipo: Artigo de Revista Científica
POR
Relevância na Pesquisa
27.96%
This work proposes the development of an Adaptive Neuro-fuzzy Inference System (ANFIS) estimator applied to speed control in a three-phase induction motor sensorless drive. Usually, ANFIS is used to replace the traditional PI controller in induction motor drives. The evaluation of the estimation capability of the ANFIS in a sensorless drive is one of the contributions of this work. The ANFIS speed estimator is validated in a magnetizing flux oriented control scheme, consisting in one more contribution. As an open-loop estimator, it is applied to moderate performance drives and it is not the proposal of this work to solve the low and zero speed estimation problems. Simulations to evaluate the performance of the estimator considering the vector drive system were done from the Matlab/Simulink(R) software. To determine the benefits of the proposed model, a practical system was implemented using a voltage source inverter (VSI) to drive the motor and the vector control including the ANFIS estimator, which is carried out by the Real Time Toolbox from Matlab/Simulink(R) software and a data acquisition card from National Instruments.

Estimador neuro-fuzzy de velocidade aplicado ao controle vetorial sem sensores de motores de indução trifásicos.; Neuro-fuzzy speed estimator applied to sensorless induction motor drives.

Lima, Fábio
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 05/07/2010 PT
Relevância na Pesquisa
28.03%
Este trabalho apresenta uma alternativa ao controle vetorial de motores de indução, sem a utilização de sensores para realimentação da velocidade mecânica do motor. Ao longo do tempo, diversas técnicas de controle vetorial têm sido propostas na literatura. Dentre elas está a técnica de controle por orientação de campo (FOC), muito utilizada na indústria e presente também neste trabalho. A principal desvantagem do FOC é a sua grande sensibilidade às variações paramétricas da máquina, as quais podem invalidar o modelo e as ações de controle. Nesse sentido, uma estimativa correta dos parâmetros da máquina, torna-se fundamental para o acionamento. Este trabalho propõe o desenvolvimento e implementação de um estimador baseado em um sistema de inferência neuro-fuzzy adaptativo (ANFIS) para o controle de velocidade do motor de indução trifásico em um acionamento sem sensores. Pelo fato do acionamento em malha fechada admitir diversas velocidades de regime estacionário para o motor, uma nova metodologia de treinamento por partição de frequência é proposta. Ainda, faz-se a validação do sistema utilizando a orientação de campo magnético no referencial de campo de entreferro da máquina. Simulações para avaliação do desempenho do estimador mediante o acionamento vetorial do motor foram realizadas utilizando o programa Matlab/Simulink. Para a validação prática do modelo...

Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

Nakai, Mauricio E.; Guillardi Júnior, Hildo; Spadotto, Marcelo M.; Aguiar, Paulo R.; Bianchi, Eduardo C.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 329-334
ENG
Relevância na Pesquisa
37.86%
This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.

Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas

Spadotto, Marcelo Montepulciano
Fonte: Universidade Estadual Paulista (UNESP) Publicador: Universidade Estadual Paulista (UNESP)
Tipo: Dissertação de Mestrado Formato: 88 f. : il.
POR
Relevância na Pesquisa
38.09%
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Pós-graduação em Engenharia Elétrica - FEB; A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materiais cerâmicos tornou possível o emprego de processos de fabricação que antes eram somente empregados em metais. Dentre os processos de usinagem de cerâmicas avançadas, a retificação é o mais utilizado devido às maiores taxas de remoção diferentemente do brunimento e das limitações geométricas do processo de lapidação. A rugosidade é um do parâmetros de saída do processo de retificação que influi, dentre outros fatores, na qualidade do deslizamento entre estruturas, podendo gerar aquecimento. Além disso, o desgaste da ferramenta de corte gerado durante o processo está associado aos custos fixos e a problemas relacionados com o acabamento superficial bem como a danos estruturais. Essas duas variáveis, rugosidade e desgaste, são objetos de estudos de muitos pesquisadores. Entretanto, o controle automático tem sido uma difícil tarefa de ser realizada devido às variações de parâmetros ocorridas no processo. Dessa maneira...

Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system

Shahbazikhah,Parviz; Asadollahi-Baboli,Mohammad; Khaksar,Ramin; Alamdari,Reza Fareghi; Zare-Shahabadi,Vali
Fonte: Sociedade Brasileira de Química Publicador: Sociedade Brasileira de Química
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2011 EN
Relevância na Pesquisa
27.54%
Food contaminations by migration of low molecular weight additives into foodstuffs can result from direct contact between packaging materials and food. The amount of migration is related to the structural properties of the additive as well as to the nature of packaging material. The goal of this study is to develop a quantitative structure-property relationship (QSPR) model by the adaptive neuro-fuzzy inference system (ANFIS) for prediction of the partition coefficient K in food/packaging system. The partition coefficients of a set of 44 systems consisted of 4 food simulants, 6 migrants and 2 packaging materials were investigated. A set of 6 molecular descriptors representing various structural characteristics of food simulants (2 descriptors), migrants (3 descriptors) and polymers (1 descriptor) was used as data set. This data set was divided into three subsets: training, test and prediction. ANFIS as a new modeling technique was applied for the first time in this field. The final model has a root mean square error (RMSE) of 0.0006 and correlation coefficient (R²) for the prediction set of 0.9920.

Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process

Petchinathan,G.; Valarmathi,K.; Devaraj,D.; Radhakrishnan,T. K.
Fonte: Brazilian Society of Chemical Engineering Publicador: Brazilian Society of Chemical Engineering
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2014 EN
Relevância na Pesquisa
27.73%
This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.

Application of ANFIS for analytical modeling of tensile strength of functionally graded steels

Nazari,Ali
Fonte: ABM, ABC, ABPol Publicador: ABM, ABC, ABPol
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2012 EN
Relevância na Pesquisa
37.73%
In the present study, the tensile strength of ferritic and austenitic functionally graded steels produced by electroslag remelting has been modeled. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with adaptive network-based fuzzy inference systems (ANFIS). To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. According to the input parameters, in the ANFIS model, the Vickers microhardness of each layer was predicted. A good fit equation which correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterwards; the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress-strain curve of each layer...

Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Khalaj,Gholamreza; Nazari,Ali; Livary,Akbar Karimi
Fonte: ABM, ABC, ABPol Publicador: ABM, ABC, ABPol
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2013 EN
Relevância na Pesquisa
37.86%
The paper presents some results of the research connected with the development of new approach based on the Adaptive Network-based Fuzzy Inference Systems (ANFIS) of predicting the Vickers microhardness of the phase constituents occurring in five steel samples after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. To construct these models, 114 different experimental data were gathered from the literature. The data used in the ANFIS model is arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the ANFIS systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels.

ANFIS-based approach for predicting sediment transport in clean sewer

Azamathulla, H. Md.; Ab. Ghani, Aminuddin; Fei, Seow Yen
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Publicado em /03/2012 EN
Relevância na Pesquisa
27.54%
The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r2 = 0.98 and RMSE = 0.002431) compared to the existing predictor.

The Design and Implementation of Adsorptive Removal of Cu(II) from Leachate Using ANFIS

Turan, Nurdan Gamze; Ozgonenel, Okan
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 11/06/2013 EN
Relevância na Pesquisa
27.86%
Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (23), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 23 experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system.

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy

Ankışhan, Haydar; Yılmaz, Derya
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
27.54%
Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS.

Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

Jhin, Changho; Hwang, Keum Taek
Fonte: MDPI Publicador: MDPI
Tipo: Artigo de Revista Científica
Publicado em 22/08/2014 EN
Relevância na Pesquisa
27.73%
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.

Identificação fuzzy-multimodelos para sistemas não lineares

Rodrigues, Marconi Câmara
Fonte: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações Publicador: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações
Tipo: Tese de Doutorado Formato: application/pdf
POR
Relevância na Pesquisa
28.33%
This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models...

Estrutura ANFIS modificada para identificação e controle de plantas com ampla faixa de operação e não linearidade acentuada

Fonseca, Carlos André Guerra
Fonte: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações Publicador: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações
Tipo: Tese de Doutorado Formato: application/pdf
POR
Relevância na Pesquisa
38.27%
In this work a modification on ANFIS (Adaptive Network Based Fuzzy Inference System) structure is proposed to find a systematic method for nonlinear plants, with large operational range, identification and control, using linear local systems: models and controllers. This method is based on multiple model approach. This way, linear local models are obtained and then those models are combined by the proposed neurofuzzy structure. A metric that allows a satisfactory combination of those models is obtained after the structure training. It results on plant s global identification. A controller is projected for each local model. The global control is obtained by mixing local controllers signals. This is done by the modified ANFIS. The modification on ANFIS architecture allows the two neurofuzzy structures knowledge sharing. So the same metric obtained to combine models can be used to combine controllers. Two cases study are used to validate the new ANFIS structure. The knowledge sharing is evaluated in the second case study. It shows that just one modified ANFIS structure is necessary to combine linear models to identify, a nonlinear plant, and combine linear controllers to control this plant. The proposed method allows the usage of any identification and control techniques for local models and local controllers obtaining. It also reduces the complexity of ANFIS usage for identification and control. This work has prioritized simpler techniques for the identification and control systems to simplify the use of the method; Neste trabalho propõe-se uma modificação na estrutura neurofuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) para a obtenção de um método sistemático para identificação e controle de plantas com ampla faixa de operação e não linearidade acentuada...

Análise e Comparação de Modelos de Previsão de Vazões para o Planejamento Energético, Utilizando Séries Temporais; Analysis and Comparison of Prediction Models for Energy Planning Flows, Using Time Series

XAVIER, Priscila Branquinho
Fonte: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia Publicador: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
27.73%
n the planning of the energetic operation, analysis and forecasts of the flow are very important. A huge difficulty in the forecast of flow is the seasonality presence, due to drought and flood periods in the year. Many scientists, with different methodologies, have been concerned with finding a best model, compared with the utilized by Brazil s system - Markovian Model. The Makovian Model, or selfregressive with order 1, is a Box & Jenkins methodology, and requires data handling to treat non-stationarity, or the use of regular models, requiring a hardly theoretical formulation for the statistical procedures. Therefore, the statistical models, autoregressive model with seasonality and Holt-Winters model, of treatment of temporal series are presented and, carried out the flow s analysis and forecast for three study groups, in two different (historical) horizons. The performance of the models was compared and the results showed that the proposed models presents better adjust than the model adopted by Brazilian system; No planejamento da operação energética, a análise e previsão de vazões são muito importantes. Uma grande dificuldade na previsão de vazões é a presença da sazonalidade, devido aos períodos de seca e cheia no ano. Muitos estudiosos...

Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System

Khanlou, H.M.; Ang, B.C.; Barzani, M.M.; Silakhori, M.; Talebian, S.
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica
Publicado em //2015 EN
Relevância na Pesquisa
27.73%
An adaptive neuro-fuzzy system (ANFIS) model was employed to predict the surface roughness. Surface roughening of titanium biomaterials has a crucial effect on increasing the biocompatibility. For this purpose, sandblasted, large-grit, acid-etched (SLA) has been introduced as an effective method to change the surface texturing and roughness. Subsequent processes—polishing, sandblasting and acid etching or SLA—were employed to modify the surface. Alumina particles for surface blasting and Kroll’s etchant (3 ml HF + 6 ml HNO₃ + 100 ml H₂O) for acid etching were utilized in this experiment. This was performed for three different periods of time (10, 20 and 30 s) and temperatures (25, 45 and 60 centigrade). Correspondingly, the Ti-13Zr-13Nb surfaces were evaluated using a field emission scanning electron microscope for texturing, contact mode profile meter for the average surface roughness (Ra) (nm) and atomic force microscopy for surface texturing at the nano-scale. In addition, the surface roughness was reduced in each condition, particularly in extremely high conditions. Significantly, the ANFIS model predicted the Ra amount of textured surface with an error band of 10 %. This research presents an idea to use the ANFIS model to obtain proper biological signs on the roughened surface in terms of surface roughness.; Hossein Mohammad Khanlou...

Técnicas inteligentes hídridas para o controle de sistemas não lineares

Rodrigues, Marconi Câmara
Fonte: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações Publicador: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Engenharia Elétrica; Automação e Sistemas; Engenharia de Computação; Telecomunicações
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
27.73%
A neuro-fuzzy system consists of two or more control techniques in only one structure. The main characteristic of this structure is joining one or more good aspects from each technique to make a hybrid controller. This controller can be based in Fuzzy systems, artificial Neural Networks, Genetics Algorithms or rein forced learning techniques. Neuro-fuzzy systems have been shown as a promising technique in industrial applications. Two models of neuro-fuzzy systems were developed, an ANFIS model and a NEFCON model. Both models were applied to control a ball and beam system and they had their results and needed changes commented. Choose of inputs to controllers and the algorithms used to learning, among other information about the hybrid systems, were commented. The results show the changes in structure after learning and the conditions to use each one controller based on theirs characteristics; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Neste trabalho é mostrado tanto o desenvolvimento quanto as características de algumas das principais técnicas utilizadas para o controle inteligente de sistemas. Partindo de um controlador fuzzy foi possível aplicar técnicas de aprendizagem, similares às utilizadas pelas Redes Neurais Artificiais (RNA's)...

Colombian Energy Market: An approach of Anfis and Clustering Techniques to an Optimal Portfolio

Palacios, Alejandro; Giraldo, Marcela; Quintero, O. L.
Fonte: Universidad EAFIT; Grupo de Investigaci??n Modelado Matem??tico; Escuela de Ciencias Publicador: Universidad EAFIT; Grupo de Investigaci??n Modelado Matem??tico; Escuela de Ciencias
Tipo: workingPaper; Documento de trabajo de investigaci??n; draft
ENG
Relevância na Pesquisa
37.54%
This paper focuses on the study of a first approach to an optimal portfolio in the Colombian Energy Market using Artificial Intelligence. Specifically, ANFIS and Clustering techniques are applied. The methodology is implemented using the Matlab Toolboxes for clustering and FIS generation. Te results are presented, as well as the analysis of them. A first approximation to an optimal portfolio obtained with this methodology is shown. Consequently, some conclusions of the different techniques available for the same purpose are discussed. Finally the future work is proposed.

Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor Detection

Sharma, Minakshi
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 30/11/2012
Relevância na Pesquisa
27.73%
Detection and segmentation of Brain tumor is very important because it provides anatomical information of normal and abnormal tissues which helps in treatment planning and patient follow-up. There are number of techniques for image segmentation. Proposed research work uses ANFIS (Artificial Neural Network Fuzzy Inference System) for image classification and then compares the results with FCM (Fuzzy C means) and K-NN (K-nearest neighbor). ANFIS includes benefits of both ANN and the fuzzy logic systems. A comprehensive feature set and fuzzy rules are selected to classify an abnormal image to the corresponding tumor type. Experimental results illustrate promising results in terms of classification accuracy. A comparative analysis is performed with the FCM and K-NN to show the superior nature of ANFIS systems.; Comment: 5 pages

Use of ANFIS Control Approach for SSSC based Damping Controllers Applied in a Two-area Power System

Murali,D.; Rajaram,M.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2013 EN
Relevância na Pesquisa
37.86%
In an interconnected power system, low frequency electromechanical oscillations are initiated by normal small changes in system loads, and they become much worse following a large disturbance. Flexible AC Transmission System (FACTS) devices are widely recognized as powerful controllers for damping power system oscillations. The standard FACTS controllers are linear controllers which may not guarantee acceptable performance or stability in the event of a major disturbance. To overcome the drawbacks of conventional controllers, ANFIS (Adaptive Neuro-Fuzzy Inference System) control scheme has been developed in this paper, and it has been applied for the external coordinated control of series connected FACTS controllers known as Static Synchronous Series Compensators (SSSCs) employed in a two-area power system. In neuro-fuzzy control method, the simplicity of fuzzy systems and the ability of training in neural networks have been combined. The training data set the parameters of membership functions in fuzzy controller. This ANFIS can track the given input-output data in order to conform to the desired controller. Simulation studies carried out in MATLAB/SIMULINK environment demonstrate that the proposed ANFIS based SSSC controller shows the improved damping performance as compared to conventional SSSC based damping controllers under different operating conditions.