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

Nabila M. El-Rabaie, Tarek A. Mahmoud
2006 JES. Journal of Engineering Sciences  
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
more » ... ze 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. KEY WORDS: Fuzzy neural networks, Wavelets, Particle swarm optimization. Recently, a new evolutionary computing method, the particle swarm algorithm (PSA), is proposed [10] . Similar to the GA, the PSA is initialized with a population of random solutions. It was developed based on the analogy of the swarm theory of bird flocking and fish schooling. Each individual in the PSA is called particle and it is assigned with a randomized velocity according its own and its companion flying aaaaaa 557 Nabila M. El-Rabaie and Tarek A. Mahmoud ________________________________________________________________________________________________________________________________ 558
doi:10.21608/jesaun.2006.110470 fatcat:l4tke57ubbc6lmoybmk7oz7ftu