8Genetic Algorithms in Fuzzy Control Systems

Fuzzy Logic Controllers Lee90] (FLCs) are being widely and successfully applied in diierent areas. Fuzzy Logic Controllers can be considered as knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions (among other information elements). The deenition of these Fuzzy Rules and Fuzzy Membership Functions is actually aaected by subjective decisions, having a great innuence over the performance of the Fuzzy Controller. From this point of view, FLCs can be interpreted as a particular type of real time expert systems. A second interpretation more adequate for the analysis of the control properties of the FLC is to think about FLCs as non-linear, time-invariant control laws. In addition, recent works have demonstrated the ability of Fuzzy Controllers to approximate continuous functions on a compact set with an arbitrary degree of precision; diierent kinds of FLCs are universal approximators ((Buc93, Cas95]). Combining ideas related to these diierent interpretations, some eeorts have been made to obtain an improvement in system performance (a better approximation to an optimal controller, with a certain performance criterion) by incorporating learning mechanisms to modify predeened rules and/or membership functions, represented as parameterized expressions. The main goal will be to combine the ability to incorporate experts' knowledge with a knowledge-based point of view (Knowledge engineering), 1 Dr. Velasco ([email protected]) is with Dept. Ingenier a de Sistemas Telemm aticos and Dr. Magdalena ([email protected]) is with Dept. Matemm atica Aplicada a las Tenolog as de la Informacii on.