This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller (T-RFCMAC) 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 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.
Fuzzy cerebellar model articulation controller, entropy, Nelder--Mead, particle swarm optimization, prediction, identification
LEE, CHIN-LING and LIN, CHENGJIAN
"An efficient recurrent fuzzy CMAC model based on a dynamic-group--based hybrid evolutionary algorithm for identification and prediction applications,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 26:
4, Article 27.
Available at: https://journals.tubitak.gov.tr/elektrik/vol26/iss4/27