GENERATING FUZZY RULES FROM EXAMPLES USING GENETIC ALGORITHMS
Francisco Herrera, Manuel Lozano, José Luís Verdegay · Advances in fuzzy systems · 1995
The problem of generating desirable fuzzy rules is very important in the development of fuzzy systems. The purpose of this paper is to present a generation method of fuzzy control rules by learning from examples using genetic algorithms. We propose a real coded genetic algorithm for learning fuzzy rules, and an iterative process to obtain a set of rules that covers the examples set with a covering value previously defined. Keywords: Fuzzy rules, learning, genetic algorithms. 1. Introduction Fuzzy rules based systems have been shown to be an important tool to model complex systems, where due to the complexity or the imprecision, classical tools are unsuccessful. The knowledge base of a fuzzy controller consists of a collection of rules describing the control actions. The performance of fuzzy control depends greatly on whether the control rules are reasonable or not. There are different modes to derive them: - Based on Expert Experience and Control Engineering Knowledge. - Based o...