A simplified model of fuzzy inference system constructed by using RBF neurons

Author

Wu, Ai ; Tam, P.K.S.

Author_Institution

Dept. of Electron. & Inf. Eng., Hong Kong Polytech., Hung Hom, Hong Kong

Volume

1

fYear

1999

fDate

22-25 Aug. 1999

Firstpage

50

Abstract

A new simplified model of fuzzy neural network is presented based on the functional equivalence relation between radial basis function (RBF) network and fuzzy inference system. The proposed network model has a lower number of the centre values of the network and is especially suitable for multivariable systems. An adaptive constructing method and some learning algorithms of the simplified model are proposed. The simulation results of a function mapping show that the simplified model of the fuzzy neural network has a satisfactory approximation ability to a nonlinear multivariable function.

Keywords

function approximation; fuzzy neural nets; fuzzy set theory; inference mechanisms; learning (artificial intelligence); radial basis function networks; adaptive constructing method; function approximation; function mapping; fuzzy inference system; fuzzy neural network; learning algorithms; multivariable systems; radial basis function network; simplified model; Biological system modeling; Electronic mail; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Inference algorithms; MIMO; Neurons; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International

Conference_Location

Seoul, South Korea

ISSN

1098-7584

Print_ISBN

0-7803-5406-0

Type

conf

DOI

10.1109/FUZZY.1999.793205

Filename

793205