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This paper presents a novel fuzzy modeling strategy using the hybrid algorithm EPPSO based on the combination of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) for control of nonlinear dynamical systems. The EPPSO is used to automatically design fuzzy controllers for nonlinear dynamical systems. In the simulation part, one multi-input multi-output (MIMO) plant control problem is performed. The performance of the suggested method is compared to that of EP, PSO and HGAPSO in the fuzzy controllers design. Simulation results demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new kind of Evolutionary Fuzzy System (EFS) based on the Least Squares (LS) method and a hybrid learning algorithm: Adaptive Evolutionary-programming and Particle-swarm-optimization (AEPPSO). The structure of the Extended Fuzzy Basis Function Network (EFBFN) is firstly proposed, and the LS method is used to design it with presetting the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel learning algorithm based on least squares and the hybrid of evolutionary programming and particle swarm optimization (AEPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. In the simulation part, the proposed method is employed to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed. [ABSTRACT FROM AUTHOR]