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This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller (TRFCMAC) model based on a dynamic-group–based hybrid evolutionary algorithm (DGHEA) for solving identification and prediction problems. The proposed T-RFCMAC model is based on the traditional CMAC model and the Takagi– Sugeno–Kang (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 (QPSO) and the Nelder–Mead method, is proposed for adjusting the parameters of the T-RFCMAC model. In DGHEA, an entropybased 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.

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