An introduction to differential evolution
New ideas in optimization
Journal of Global Optimization
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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This paper presents a novel fuzzy modeling strategy based on the hybrid of particle swarm optimization (PSO) and differential evolution (DE), and the proposed hybrid algorithm is referred to as PSODE. PSODE is based on a two-population scheme, in which the individuals in one population is enhanced by PSO and the individuals in the other population is evolved by DE. The individuals both in PSO and DE are co-evolved during the algorithm execution by employing an information sharing mechanisms. To further improve the proposed PSODE algorithm a nonlinear inertia weight approach and a mutation mechanism are presented respectively. In the simulation part, the PSODE is used to automatic design of fuzzy identifier for a nonlinear dynamic system. The performance of the suggested method is compared to PSO, DE and some other methods in the fuzzy identifier design to demonstrate its superiority.