Generating and tuning fuzzy rules using hybrid systems

In this paper we present different approaches to the problem of fuzzy rule extraction by using a combination of fuzzy clustering and genetic algorithms as the main tools. This combination of techniques allows one to define a hybrid system by which one can have different approaches in a fuzzy modeling process. For example, one can obtain a first approximation to the fuzzy rules that describe the system behavior represented by a collection of raw data, without any assumption about the structure of the data by using the fuzzy clustering technique, and subsequently these rules can be tuned using the genetic algorithm. Alternatively this genetic algorithm can be used in order to generate and tune the fuzzy rules directly from the data without or with some priori information. Finally, their performances are compared.