An efficient recurrent fuzzy CMAC model based on a dynamic-group--based hybrid evolutionary algorithm for identification and prediction applications

Authors : Chinling LEE, Chengjian LIN

Pages : 2003-2015

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller insert ignore into journalissuearticles values(T-RFCMAC); model based on a dynamic-group--based hybrid evolutionary algorithm insert ignore into journalissuearticles values(DGHEA); for solving identification and prediction problems. The proposed T-RFCMAC model is based on the traditional CMAC model and the Takagi--Sugeno--Kang insert ignore into journalissuearticles values(TSK); parametric fuzzy inference system. Otherwise, the recurrent network, which imports feedback links with a receptive field cell, is embedded in the T-RFCMAC model, and the feedback units are used as memory elements. The DGHEA, which is a hybrid of the dynamic-group quantum particle swarm optimization insert ignore into journalissuearticles values(QPSO); and the Nelder--Mead method, is proposed for adjusting the parameters of the T-RFCMAC model. In DGHEA, an entropy-based grouping technique is adopted to improve the searching capability and the convergent speed of quantum particles swarm optimization. Experimental results show that the proposed DGHEA-based T-RFCMAC model is more effective at identification and prediction than other models.
Keywords : Fuzzy cerebellar model articulation controller, entropy, Nelder Mead, particle swarm optimization, prediction, identification