Neural network implementation of a new fuzzy system

Author

Chak, Chu Kwong ; Feng, Gang

Author_Institution

Sch. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia

fYear

1994

fDate

29 Nov-2 Dec 1994

Firstpage

194

Lastpage

198

Abstract

The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimise its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and backpropagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions

Keywords

backpropagation; fuzzy logic; fuzzy neural nets; least squares approximations; back error propagation; backpropagation; consequence parameters; convergence; data pairs; fuzzy logic system; high order fuzzy system; learning scheme; least squares estimation; membership functions; neural network implementation; new fuzzy system; nonlinear function modelling; premise parameters; Automatic control; Control systems; Fuzzy control; Fuzzy logic; Fuzzy systems; Humans; Least squares approximation; Neural network hardware; Neural networks; Parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on

Conference_Location

Brisbane, Qld.

Print_ISBN

0-7803-2404-8

Type

conf

DOI

10.1109/ANZIIS.1994.396922

Filename

396922