Generalized fuzzy RBF networks and nonlinear system identifications

Author

Hong, Bao ; Yun, Xie ; Xinkuo, Chen

Author_Institution

Fac. of Autom., Guangdong Univ. of Technol., Guangzhou, China

Volume

3

fYear

2002

fDate

2002

Firstpage

2508

Abstract

Based on summing up three kinds of fuzzy inference systems and the functional equivalence between the radial basis function (RBF) networks and fuzzy inference systems, the paper presents a new concept of generalized fuzzy inference and the new model of generalized fuzzy RBF network. Then the generalized learning algorithm is derived. A nonlinear system identification is done by this network. Results have verified that the generalized fuzzy RBF networks have an ability to approximate arbitrary nonlinear function with an arbitrary given accuracy and the learning algorithm described in the paper is effective and available.

Keywords

fuzzy logic; fuzzy neural nets; identification; inference mechanisms; learning (artificial intelligence); nonlinear systems; radial basis function networks; arbitrary nonlinear function; functional equivalence; fuzzy inference systems; generalized fuzzy RBF networks; generalized fuzzy inference; generalized learning algorithm; nonlinear system identifications; radial basis function networks; Automation; Equations; Fault diagnosis; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Nonlinear systems; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on

Print_ISBN

0-7803-7268-9

Type

conf

DOI

10.1109/WCICA.2002.1021546

Filename

1021546