Design of neuromorphic fuzzy controllers

Author

Pham, D.T. ; Karaboga, D.

Author_Institution

Intelligent Syst. Res. Lab., Univ. of Wales, Cardiff, UK

fYear

1993

fDate

17-20 Oct 1993

Firstpage

103

Abstract

The paper introduces a general neural model for a SISO fuzzy logic controller (FLC). An FLC represented in the form of a neural network can be trained using a genetic algorithm (GA). This enables the simultaneous determination of the membership functions for the fuzzy input variable, the quantisation levels for the output variable and the elements of the relation matrix of the FLC. The paper presents simulation results for the control of a time delayed second order system which show the fast and accurate performance of a GA trained neuromorphic FLC

Keywords

computerised control; fuzzy control; fuzzy neural nets; fuzzy set theory; genetic algorithms; learning (artificial intelligence); neurocontrollers; GA trained neuromorphic FLC; SISO fuzzy logic controlle; fuzzy input variable; general neural model; genetic algorithm; membership functions; neuromorphic fuzzy controllers; output variable; quantisation levels; relation matrix; time delayed second order system control; Educational institutions; Fuzzy control; Fuzzy logic; Genetic algorithms; Input variables; Intelligent systems; Laboratories; Neural networks; Neuromorphics; Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on

Conference_Location

Le Touquet

Print_ISBN

0-7803-0911-1

Type

conf

DOI

10.1109/ICSMC.1993.390691

Filename

390691