The preference of Fuzzy Wavelet Neural Network to ANFIS in identification of nonlinear dynamic plants with fast local variation

Abstract

This paper presents a Fuzzy Wavelet Neural Network (FWNN) for identification of a system with fast local variation. The FWNN combines wavelet theory with fuzzy logic and neural networks. An effective clustering algorithm is used to initialize the parameters of the FWNN. Learning fuzzy rules in this FWNN is based on gradient decent method. The performance of the FWNN structure is illustrated by applying to a nonlinear dynamic plant which has fast local variation then compared with Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Simulation results indicate remarkable capabilities of the proposed identification method for plants with fast local variation.