Title :
Fuzzy Modeling Using Chaotic Particle Swarm Approaches Applied to a Yo-yo Motion System
Author :
Coelho, Leandro Dos Santos ; Herrera, Bruno Meirelles
Author_Institution :
Pontifical Catholic Univ. of Parana, Curitiba
Abstract :
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.
Keywords :
control system synthesis; fuzzy control; fuzzy systems; identification; least mean squares methods; motion control; nonlinear control systems; particle swarm optimisation; pattern clustering; Gustafson-Kessel clustering algorithm; Takagi-Sugeno fuzzy system modeling; Zaslavskii chaotic map sequence; chaotic particle swarm optimization approach; least mean squares technique; nonlinear identification; nonlinear yo-yo motion control system; production rule; Algorithm design and analysis; Chaos; Clustering algorithms; Fuzzy control; Fuzzy systems; Motion control; Optimization methods; Particle production; Particle swarm optimization; Takagi-Sugeno model;
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
DOI :
10.1109/FUZZY.2006.1682018