A Three-Stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy-Logic-Controller Knowledge Bases From Examples

Abstract

Nowadays fuzzy logic controllers have been successfully applied to a wide range of engineering control processes. Several tasks have to be performed in order to design an intelligent control system of this kind for a concrete application. One of the most important and difficult ones is the extraction of the expert known knowledge of the controlled system. The aim of this paper is to present an evolutionary process based on genetic algorithms and evolution strategies for learning the fuzzy-logic-controller knowledge base from examples in three different stages. The process allows us to generate two different kinds of knowledge bases, descriptive and approximate ones, depending on the scope of the fuzzy sets giving meaning to the fuzzy-control-rule linguistic terms, taking preliminary linguistic-variable fuzzy partitions as a base. The performance of the method proposed is shown by measuring the accuracy of the fuzzy logic controllers designed in the fuzzy modeling of three three-dimensional surfaces presenting different characteristics, and by comparing them with others generated by means of three methods based on Wang and Mendel's knowledge-base generation process.