Identification and Control of Dynamic Plants Using Fuzzy Wavelet Neural Networks

Abstract

This paper presents a fuzzy wavelet neural network (FWNN) for identification and control of a dynamic plant. The FWNN is constructed on the basis of fuzzy rules that incorporate wavelet functions in their consequent parts. The architecture of the control system is presented and the parameter update rules of the system are derived. Learning rules are based on the gradient decent method and genetic algorithm (GA). The structure is tested for the identification and the control of the dynamic plants commonly used in the literature. It is shown that the proposed structure results in a better performance despite its smaller parameter space.