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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. [ABSTRACT FROM AUTHOR]

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods. [ABSTRACT FROM AUTHOR]

The article presents a study on the adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) in solving control problems. It outlines the advantages and disadvantages of the ANFN, which motivated to the development of the MODE learning algorithm. It highlights the development of the hardware-in-the loop control technique for the ANFN controller by using MODE learning algorithm (ANFN-MODE), which has been used in planetary-train-type inverted pendulum system, and the magnetic visual simulation. The study has determined the positive result of the proposed ANFN-MODE controller.