Designing fuzzy logic systems

Abstract

We present a formulation of a fuzzy logic system (FLS) that can be used to construct nonparametric models of nonlinear processes, given only input-output data. In order to effectively construct such models, we discuss several design methods with different properties and features. We compare and illustrate systems designed with each one of the methods, using an example on the predictive modeling of a nonlinear dynamic (chaotic) system.

On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering

“…Recently, they have attracted more and more attention from researchers and been analyzed and discussed in advance. Type-2 fuzzy sets have been widely applied to areas such as decision theory [6], signal processing [7], speech recognition [8], transport scheduling [9], pattern recognition [10], correlation coefficient [11], forecasting of time series [12], fuzzy equation systems [13], and so forth.…”

Design and Implementation of Fuzzy Approximation PI Controller for Automatic Cruise Control System

“…Fuzzy logic controllers (FLC) have gained high appreciation in nonlinear control [1,2] although Proportional-IntegralDerivative (PID) controllers and model predictive controllers (MPC) are widely used in industrial processes [3][4][5]. This is mainly due to its aptitude which can counter nonlinear control problems by programming heuristic knowledge.…”

Previsões Pontuais E Intervalares De Séries Temporais De Alta Frequência Com Sistema De Lógica Fuzzy

“…O mecanismo de inferência fuzzy combina as regras fuzzy para realizar um mapeamento do conjunto fuzzy da entrada, no espaço da entrada , no conjunto fuzzy de saída, no espaço da saída , utilizando operações de lógica fuzzy [18,128,170]. 11 Para uma regra , e para a entrada -dimensional mapeada no conjunto fuzzy * , o conjunto de saída será determinado como o conjunto = * ∘ .…”

“…O defuzzificador transforma o conjunto fuzzy de saída do mecanismo de inferência em um valor numérico [128]. Os métodos de defuzzificação incluem os defuzzificadores baseados no máximo valor do conjunto fuzzy de saída (maximum defuzzifier), na média dos máximos (mean of maxima defuzzifier), no centroide do conjunto fuzzy (centroid defuzzifier), na altura (height defuzzifier), e outros (ver [18,171]).…”

“…Os sistemas de lógica fuzzy, compostos pelos elementos descritos na Seção 3.1., podem ser construídos a partir de inúmeros métodos [128]. O método utilizado para construir o modelo aplicado neste trabalho é o método Wang-Mendel [38].…”