Parallel genetic algorithms for a neurocontrol problem

This paper addresses stabilization for Takagi-Sugeno (T-S) fuzzy systems with model uncertainties via a socalled fuzzy Lyapunov function, which is a multiple Lyapunov function. Based on the fuzzy Lyapunov function approach and a parallel distributed compensation (PDC) scheme, we provide stabilization conditions for closed-loop fuzzy systems with model uncertainties. Furthermore, we propose a compound search strategy composed of island random optimal algorithms concatenated with the Simplex method to identify the chaotic systems, and to solve the linear matrix inequality (LMI) problem. Finally, a numerical example of the Lorenz system is given to illustrate the utility of the proposed approach. Keywords-Fuzzy Lyapunov function, Linear matrix inequality, Model uncertainty, Random optimal algorithmsI.

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Parallel genetic algorithms for a neurocontrol problem

Cited by 5 publications

References 3 publications

Robust fuzzy control and evolutionary fuzzy identification of singularly perturbed nonlinear systems with parameter uncertainty

Robust fuzzy control and evolutionary fuzzy identification of singularly perturbed nonlinear systems with parameter uncertainty

Relax the chaos-model-based human behavior by electrical stimulation therapy design

A fuzzy Lyapunov function approach to stabilize uncertain nonlinear systems using improved random search method

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