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

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.

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.