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

Abstract

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 identificationand 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 feedbacklinks with a receptive field cell, is embedded in the T-RFCMAC model, and the feedback units are used as memoryelements. The DGHEA, which is a hybrid of the dynamic-group quantum particle swarm optimization (QPSO) andthe 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 particlesswarm optimization. Experimental results show that the proposed DGHEA-based T-RFCMAC model is more effectiveat identification and prediction than other models.