Constructing a fuzzy expert system using the ILFN network and the Genetic Algorithm

被引：0

作者：

Yen, GG ^{[1
]}

Meesad, P ^{[1
]}

机构：

[1] Oklahoma State Univ, Intelligent Syst & Control Lab, Stillwater, OK 74078 USA

来源：

SMC 2000 CONFERENCE PROCEEDINGS: 2000 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOL 1-5 | 2000年

关键词：

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a method for automatic construction of a fuzzy expert system from numerical data using the ILFN network and the 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. In this paper, 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.

引用

页码：1917 / 1922

页数：6

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