Nonlinear System Identification Based on TS-GFNN

Author

Wei, Ruihua ; Xu, Lihong

Author_Institution

Dept. of Control Sci. & Eng., Tongji Univ., Shanghai

fYear

2006

fDate

5-8 Dec. 2006

Firstpage

1

Lastpage

5

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

Keywords

control system synthesis; fuzzy control; neurocontrollers; nonlinear control systems; parameter estimation; pattern clustering; Takagi-Sugeno-generalized fuzzy neural network; clustering based membership function; controller design; fuzzy logic system; nonlinear system identification; online identification algorithm; parameter identification; Algorithm design and analysis; Clustering algorithms; Fuzzy logic; Fuzzy neural networks; Neural networks; Nonlinear systems; Parameter estimation; Power system modeling; Programmable control; Takagi-Sugeno model; GFNN (Generalized Fuzzy Neural Network); Identification for Nonlinear System; On-line Identification; T-S Fuzzy Model;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on

Conference_Location

Singapore

Print_ISBN

1-4244-0341-3

Electronic_ISBN

1-4214-042-1

Type

conf

DOI

10.1109/ICARCV.2006.345131

Filename

4150041