Constructing a kind of fuzzy systems based on neural networks techniques

Author

Qing, Ming ; Zhao, Hai-Liang ; Xia, Shi-Fen ; Wang, Xue-Fang

Author_Institution

Dept. of Math., Southwest Jiaotong Univ., Sichuan, China

Volume

5

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

2629

Abstract

In this paper, a method to model a kind of nonlinear fuzzy system with fuzzy border is presented. BP networks (BPN) possess efficient capability of approximating nonlinear function and radius base function networks (RBFN) have the fast training speed. These advantages of BPN and RBFN can be combined with clustering techniques to improve system modeling. Firstly, the system structure is obtained by clustering. Secondly the BPN is employed to generate rule base´s antecedent function and RBFN to approximate each rule´s conclusion function, respectively. So the initial construction of the system can be acquired. Thirdly, structure design and training of networks are discussed in detail. Finally, the structure optimization and overstudy of RBFN are discussed.

Keywords

backpropagation; function approximation; fuzzy systems; nonlinear systems; optimisation; radial basis function networks; BP networks; BPN; RBFN; clustering techniques; function approximation; fuzzy border; networks structure design; networks training; neural networks techniques; nonlinear fuzzy system; radius base function networks; rule bases antecedent function; structure optimization; system modeling; system structure; Control system synthesis; Electronic mail; Function approximation; Fuzzy control; Fuzzy systems; Mathematical model; Mathematics; Modeling; Neural networks; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259974

Filename

1259974