Abstract—In this paper, a fuzzy cerebellar model articulation controller (FCMAC) model with learning ability is proposed for solving the time series prediction problem. An efficient learning algorithm, called symbiotic particle swarm optimization (SPSO), combined symbiotic evolution and modified particle swarm optimization for tuning parameters of the FCMAC. Simulation results show that the converging speed and root mean square error (RMS) of the proposed method has a better performance than those of other methods.
Index Terms—Cerebellar model articulation controller, fuzzy set, particle swarm optimization, symbiotic evolution, time series, prediction.
C. L. Lee is with the International Trade Department, National Taichung University of Science and Technology, Taichung City, Taiwan 404, ROC. (e-mail: merrylee@nutc.edu.tw).
C. J. Lin is with the Computer Science and Information Engineering Department, National Chin-Yi University of Technology, Taichung City, Taiwan 411, ROC. (e-mail:cjlin@ncut.edu.tw).
Cite: Chin-Ling Lee and Cheng-Jian Lin, "Time Series Prediction Using Fuzzy Cerebellar Model Articulation Controller with Symbiotic Particle Swarm Optimization," International Journal of Information and Education Technology vol. 3, no. 2, pp. 235-239, 2013.