Parameters Learning Approach for Generalized Takagi-Sugeno Fuzzy Model Using Particle Swarm Optimization

A parameters learning approach for generalized takagi-sugeno (T-S) fuzzy model is proposed in this paper on the base of analysis of generalized T-S Fuzzy model. The structural parameters of the approach are denoted by the position of discrete binary particles and the parameters of membership function in the approach are denoted by the position of ordinary particles. The combination of positions of the two kind of particles composes complete premise parameters set of a model. A learning cycle consists of two phases:first, all reasoning parameters are adjusted by evolutionary iteration of particle swarm; second, all consequent parameters are estimated through orthogonal least square error algorithm. The method requests scarcely any previous information about objects, take less calculating time, and is able to obtain compact fuzzy model. The simulation result shows the validity of the approach.