A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples

Nowadays Fuzzy Logic Controllers have been succesfully 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 diicult 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 diierent stages. The process allows us to generate two diierent kinds of Knowledge Bases, descriptive and approximative ones, depending on the scope of the fuzzy sets giving meaning to the fuzzy control rule linguistic terms, taking preliminary linguistic variable 1 2 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 diierent characteristics, and by comparing them with others generated by means of three methods based on Wang and Mendel's Knowledge Base generation process.