Reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm

Author

Ichimura, Takumi ; Takano, Takeshi ; Tazaki, Eiichiro

Author_Institution

Dept. of Control & Syst. Eng., Toin Univ. of Yokohama, Japan

Volume

4

fYear

1995

fDate

22-25 Oct 1995

Firstpage

3269

Abstract

In this paper, we present a reasoning and learning method for fuzzy rules using neural networks with adaptive structured genetic algorithm. This adaptive structured genetic algorithm can determine the network structure and their weights solely by an evolutionary process. With this approach, no a priori assumptions about topology are needed and the only information required is the input and output characteristics of the task. The adaptive structured genetic algorithm can generate or annihilate the specified units respectively in hidden layer to achieve an overall good system, without using back propagation or any other learning algorithm

Keywords

fuzzy logic; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); adaptive structured genetic algorithm; fuzzy rules; learning method; neural networks; reasoning; Adaptive control; Adaptive systems; Fuzzy control; Fuzzy neural networks; Fuzzy reasoning; Genetic algorithms; Learning systems; Neural networks; Neurons; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.538289

Filename

538289