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This paper introduces the Particle Swarm Algorithm (PSA)-based Local Model network (LMN) for modeling and controlling dynamic systems. Structurally, the proposed PSA-LMN merges the fuzzy set theory and wavelets in a unified form. Learning this network comprises two phases, structure learning phase and parameters learning phase. The former is performed using the Adaptive Resonance Theory (ART) algorithm while the latter is performed using the PSA. The PSA is employed to optimize parameters of the fuzzy sets, the wavelets and the free weights of the proposed LMN. Two simulation nonlinear plants are used to test the soundness of the proposed network; one is a single input single output nonlinear plant and the other is multi-variable medical plant. The latter is employed to test the proposed network in control purposes compared with Genetic Algorithm (GA)-based LMN. Better results were obtained using the proposed PSA-based LMN.

MULTIVARIABLE DYNAMIC SYSTEMS MODELING AND CONTROL USING A NEW PARTICLE SWARM ALGORITHM-LOCAL MODEL NETWORK