Nonlinear System Identification Based on TS-GFNN

Abstract

A new design of GFNN (Generalized Fuzzy Neural Network) based on T-S (Takagi-Sugeno) model and its corresponding off-line and on-line architecture and parameter identification algorithm are presented. The TS-GFNN, which integrates the advantages of Neural Network into that of the Fuzzy Logic System, is a powerful method in the modeling of the nonlinear system. Clustering based membership function is introduced in the premise of TS-GFNN, which make the architecture more concise. The on-line identification algorithm can make the TS-GFNN to be more adaptive in the design of controller. The simulation shows that the identifier based on TS-GFNN can approach the non-linear function in any precision, and it is more effective than the ordinary method.