Fuzzy identification and controller based on generalized fuzzy radial basis function networks

Abstract

This paper first proposes a new kind of fuzzy neural networks-generalized fuzzy radial basis function networks (f-RBF), which combines the fuzzifying and defuzzifying processes into a united network structure. We then give the dynamic training rule and training strategy for the f-RBF. We further discuss several special features of this kind of networks that conventional neural networks do not have, and conclude that it can process both the fuzzy-valued and real-valued data simultaneously, and can achieve the minimum realization of fuzzy controller for nonlinear systems. Finally, using the f-RBF, we design a fuzzy controller for a nonlinear system regulation. Furthermore, we point out that any nonlinear control u can be decomposed into three parts: a fuzzy control u/sub f/, a linear control u/sub l/, and an error compensation u/sub e/, i.e., u=u/sub f/+u/sub l/+u/sub e/. The stability of the closed-loop system is also analyzed using sliding control techniques. >