Constructing a fuzzy expert system using the ILFN network and the genetic algorithm

Author

Yen, Gary G. ; Meesad, Phayung

Author_Institution

Intelligent Syst. & Control Lab., Oklahoma State Univ., Stillwater, OK, USA

Volume

3

fYear

2000

fDate

2000

Firstpage

1917

Abstract

A method for automatic construction of a fuzzy expert system from numerical data using the ILFN network and a genetic algorithm is presented. The incremental learning fuzzy neural (ILFN) network was developed for pattern classification problems. The ILFN network is a fast, one-pass, on-line, and incremental learning algorithm. A knowledge base for fuzzy expert systems is extracted from the hidden units of the ILFN classifier. The genetic algorithm is then used, in an iterative manner, to reduce the number of rules and select important input pattern features needed to generate a comprehensible fuzzy rule-based system

Keywords

expert systems; fuzzy neural nets; genetic algorithms; knowledge engineering; learning (artificial intelligence); ILFN network; fuzzy expert system; fuzzy rule-based system; genetic algorithm; hidden units; incremental learning fuzzy neural network; input pattern features; knowledge base; numerical data; pattern classification; Data mining; Fuzzy logic; Fuzzy neural networks; Fuzzy set theory; Fuzzy systems; Genetic algorithms; Humans; Hybrid intelligent systems; Machine learning; Pattern classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2000 IEEE International Conference on

Conference_Location

Nashville, TN

ISSN

1062-922X

Print_ISBN

0-7803-6583-6

Type

conf

DOI

10.1109/ICSMC.2000.886393

Filename

886393