Rede Neuro-Fuzzy-Wavelet para detecção e classificação de anomalias de tensão em sistemas elétricos de potência

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Network congestion management by fuzzy inference using virtual flows

Effective network overload alleviation is very much essential in order to maintain security and integrity from the operational viewpoint of deregulated power systems. This paper aims at developing a methodology to reschedule the active power generation from the sources in order to manage the network congestion under normal/contingency conditions. An effective method has been proposed using fuzzy rule based inference system. Using virtual flows concept, which provides partial contributions/counter flows in the network elements is used as a basis in the proposed method to manage network congestions to the possible extent. The proposed method is illustrated on a sample 6 bus test system and on modified IEEE 39 bus system.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

A fuzzy rule-based decision support system for Duodopa treatment in Parkinson

A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia

Pós-graduação em Engenharia Elétrica - FEIS

Localização de descargas parciais em transformadores de potência por meio de sensores piezelétricos de baixo custo e sistemas inteligentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Localização de descargas parciais em transformadores de potência por meio de sensores piezelétricos de baixo custo e sistemas inteligentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

Vibration reduction for vision systems on board unmanned aerial vehicles using a neuro-fuzzy controller

In this paper, an intelligent control approach based on neuro-fuzzy systems performance is presented, with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. A scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrimental influence. Different trials that consider a neuro-fuzzy approach as a fundamental part of an intelligent semi-active control strategy have been carried out. Satisfactory results have been achieved compared to those obtained by means of vibration reduction passive techniques.

General Regression Neuro–Fuzzy Network for Identification of Nonstationary Plants

General Regression Neuro-Fuzzy Network, which combines the properties of conventional General Regression Neural Network and Adaptive Network-based Fuzzy Inference System is proposed in this work. This network relates to so-called “memory-based networks”, which is adjusted by one-pass learning algorithm.

Fuzzy inferring mathematical morphology and optical implementation

Fuzzification is introduced into gray-scale mathematical morphology by using two-input one-output fuzzy rule-based inference systems. The fuzzy inferring dilation or erosion is defined from the approximate reasoning of the two consequences of a dilation or an erosion and an extended rank-order operation. The fuzzy inference systems with numbers of rules and fuzzy membership functions are further reduced to a simple fuzzy system formulated by only an exponential two-input one-output function. Such a one-function fuzzy inference system is able to approach complex fuzzy inference systems by using two specified parameters within it-a proportion to characterize the fuzzy degree and an exponent to depict the nonlinearity in the inferring. The proposed fuzzy inferring morphological operators tend to keep the object details comparable to the structuring element and to smooth the conventional morphological operations. Based on digital area coding of a gray-scale image, incoherently optical correlation for neighboring connection, and optical thresholding for rank-order operations, a fuzzy inference system can be realized optically in parallel. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

Uso de neuro-fuzzy na avaliação da suscetibilidade de escorregamento de taludes.

A Presente dissertação apresenta uma aplicação de Inteligência Computacional na área de Geotecnia, com a utilização da Técnica de Neuro-Fuzzy para indicar a suscetibilidade de escorregamento de taludes no município do Rio de Janeiro, a partir de inspeção visual. Neste trabalho, a suscetibilidade corresponde à possibilidade de ocorrência de escorregamento sem considerar os danos relacionados ao evento. Adotou-se como variável de saída a Previsão de Escorregamento (PE) com três adjetivos que correspondem a Suscetibilidades Alta, Média e Baixa. A metodologia utilizada consistiu em, inicialmente, montar um banco de dados com informações preliminares de análise de estabilidade, com a indicação dos condicionantes de escorregamento relacionados à geomorfologia, pluviosidade, capacidade de drenagem, vegetação e ocupação com seus respectivos graus de suscetibilidades de escorregamento obtidos em um conjunto de Laudos de Vistoria da Geo Rio. O banco de dados foi aplicado em um algoritmo de Neuro-Fuzzy. Diversos testes foram realizados com as alterações dos parâmetros do modelo Neuro-Fuzzy para uma combinação de fatores condicionantes de escorregamento e refinamento do banco de dados. Os testes apresentaram diminuição do erro fornecido pelo programa com o aumento de tipos de condicionantes utilizados no treinamento, o que permite inferir que o escorregamento ocorre por uma complexa relação entre diversos fatores condicionantes. O banco de dados utilizado nos testes apresenta descontinuidades nas relações entre os diversos condicionantes, ou seja, para uma mesma faixa de valores de Altura do talude, não é possível obter uma relação para todas as faixas de outro condicionante e, até mesmo, para todas as faixas da Previsão de Escorregamento. As PEs obtidas na validação do modelo tiveram seus valores próximos aos desejados somente nos conjuntos de variáveis utilizadas para o treinamento. O modelo não foi capaz de apresentar valores de suscetibilidades dentro da faixa de valores utilizados no treinamento para combinação de variáveis com pequenos ruídos, o que indica a necessidade de ampliação do banco de dados tanto quantitativamente quanto qualitativamente de modo a cobrir as descontinuidades apresentadas nas relações entre as variáveis.