Fuzzy Modeling Using Chaotic Particle Swarm Approaches Applied to a Yo-yo Motion System

A method of nonlinear identification based on the Takagi-Sugeno (TS) fuzzy model and optimization procedure is proposed in this paper. New chaotic particle swarm optimization algorithms based on Zaslavskii chaotic map sequences combined with efficient Gustafson-Kessel (GK) clustering algorithm are proposed here for the design of the premise part of production rules, while the least mean squares technique is utilized for the subsequent part of the production rules of a TS fuzzy model. The numerical results presented here indicate that the particle swarm optimization (PSO) and particularly the chaotic PSO combined with GK algorithms are effective in building a good TS fuzzy model for nonlinear identification of a nonlinear yo-yo motion control system.