A Dynamic Clustering Algorithm Based on PSO and Its Application in Fuzzy Identification

A dynamic clustering algorithm based on Particle Swarm Optimization (PSO) algorithm is proposed, in which a novel coding and operation on the basis of standard PSO is introduced and DB Index rule is used to determine the validity of clustering. The simulation results illustrate its veracity and efficiency. In the first place, the proper fuzzy rule number and exact premise parameters can be obtained by using the dynamic clustering algorithm to identify fuzzy models, and result parameters by the least squared method (LSM). The effectiveness and practicability is demonstrated by the simulation results of the Box-Jenkins gas furnace data comparing with other methods.