Learning laws for neural-network implementation of fuzzy control systems

Author

Wang, Fei-Yue ; Chen, Deqian David

Author_Institution

Dept. of Syst. & Ind. Eng., Arizona Univ., Tucson, AZ, USA

Volume

2

fYear

1994

fDate

2-5 Oct 1994

Firstpage

1803

Abstract

A method of designing adaptive fuzzy control systems using structured neural networks is discussed. The basic idea is to implement a rule-based fuzzy control system with a neural network consisting of two subnetworks of pattern recognition, and fuzzy reasoning and control synthesis. The neural network is arranged such that the structure and operations of the original fuzzy control system can be fully retrieved from its network implementation. Equipped with the learning capability of neural networks, this implementation provides a mechanism to refine the existing rules and generate new rules for fuzzy control. It also opens a way for fuzzy control systems to exploit neural networks for carrying out their inherent parallel computation and suppressing the memory space required by their knowledge bases and inference programs

Keywords

adaptive control; control system synthesis; fuzzy control; learning (artificial intelligence); neural nets; pattern recognition; adaptive fuzzy control system design; control synthesis; fuzzy reasoning; inference programs; knowledge bases; learning laws; memory space suppression; neural-network implementation; parallel computation; pattern recognition; rule-based fuzzy control system; structured neural networks; subnetworks; Adaptive control; Adaptive systems; Control system synthesis; Design methodology; Fuzzy control; Fuzzy reasoning; Network synthesis; Neural networks; Pattern recognition; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2129-4

Type

conf

DOI

10.1109/ICSMC.1994.400112

Filename

400112