Particle swarm optimization for fuzzy membership functions optimization

The use of fuzzy logic to solve control problems have been increasing considerably in the past years. The successfulness of fuzzy application depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. One way to improve the performance of the fuzzy reasoning model is the use of genetic algorithm. In this paper it is shown that a particle swarm optimization (PSO) algorithm learning mechanism, supplements the performance of fuzzy reasoning model. The PSO is able to generate an optimal set of parameters for fuzzy reasoning model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a strategy for the membership functions automatic adjustment, using PSO algorithms, and presents an application designed to park a vehicle into a garage, beginning from any start position.