System Identification Based on Particle Swarm Optimization Algorithm

Regarding the problem of conducting system identification with sample data, a new identification method that typical mathematical models constitute a system model through intercombination is studied. The basic theory of this method is: transform the problem of system structure identification into a problem of combinatorial optimization, and then use the particle swarm optimization (PSO) algorithm to realize both structure and parameter identification of the system at the same time. In order to further enhance the identification capability of the PSO algorithm, an improved PSO algorithm is used for system identification. The identification algorithm given in this paper has been proved to be reasonable and effective by results of analog simulation and of actual system identification.