Rule extraction through fuzzy modeling using fuzzy neural network

Author

Matsushita, S. ; Furuhashi, T. ; Tsutsui, H.

Author_Institution

Nagoya Municipal Ind. Res. Inst., Japan

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

35

Abstract

Presents a rule extraction method from data using fuzzy neural networks (FNNs) and a genetic algorithm (GA). This method is based on a new framework for fuzzy modeling. This framework consists of a hypothesis generation block and a hypothesis evaluation block. The generation block, using GA, searches for the set of rules by generating candidates and the evaluation block guides the direction of the GA search. The FNN is used to fine tune the obtained fuzzy rules. A numerical experiment is done to show the feasibility of the proposed method

Keywords

fuzzy logic; fuzzy neural nets; genetic algorithms; inference mechanisms; modelling; fuzzy modeling; fuzzy neural network; genetic algorithm; hypothesis evaluation block; hypothesis generation block; rule extraction; Data mining; Electronic mail; Electronics industry; Fuzzy neural networks; Industrial electronics; Inference algorithms; Input variables; Inverse problems; Neural networks; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682232

Filename

682232