Parameter Estimation of the MISO Nonlinear System Based on Improved Particle Swarm Optimization

Abstract:

Nonlinear system identification is a main topic of modern identification. This paper presents a new parameter estimation method of MISO (multiple inputs, single output) Hammerstein model by using improved particle swarm optimization (IPSO). The basic idea of the method is that the model identification problem is converted into optimization of nonlinear function over parameter space. And the swarm intelligence method is used to search the parameter space concurrently and efficiently in order to find the optimal estimation of the model parameter. The basic algorithms of IPSO and the parameter control are discussed. Simulation results demonstrate effectiveness of the suggested method. The advantages of IPSO are easy to implement, few parameters to adjust, small population size, quick convergence ability and so on. Especially in high noise disturbance condition, the results of IPSO are also satisfactory.

• Periodical: Applied Mechanics and Materials (Volumes 130-134)
• Pages: 2563-2567
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.130-134.2563
• Online since: October 2011
• Authors: Huai Ke Fan, Wei Xing Lin
• Keywords: Hammerstein Model, Identification, Improved Particle Swarm Optimization (IPSO), MISO, Standard PSO

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