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Journals & Magazines >IEEE Transactions on Fuzzy Sy... >Volume: 9 Issue: 4

Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

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O. Cordon; F. Herrera; P. Villar
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Abstract:

A method is proposed to automatically learn the knowledge base by finding an appropiate data base by means of a genetic algorithm while using a simple generation method t...Show More

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Abstract:

A method is proposed to automatically learn the knowledge base by finding an appropiate data base by means of a genetic algorithm while using a simple generation method to derive the rule base. Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 9, Issue: 4, August 2001)
Page(s): 667 - 674
Date of Publication: 07 August 2002

ISSN Information:

DOI: 10.1109/91.940977
References is not available for this document.
Contents

I. Introduction

The generation of the knowledge base (KB) of a fuzzy rule-based system (FRBS) presents several difficulties because the KB depends on the concrete application, and this makes the accuracy of the FRBS directly depend on its composition.

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