Application of improved particle swarm optimization in system identification

Abstract

Particle swarm optimization (PSO) has been widely used in the fields of function optimization and neural network training because it's simple, easy to implement, has less parameters. The selection of algorithm parameters is very important to the speed optimization and results of the algorithm optimization. PSO is easy to fall into the local optimal value and when the number of particles is less, the optimization accuracy is greatly reduced. In addition, local search capability and global search capability are mutually restricted. According to above disadvantages Improved Particle Swarm Optimization based on the Gauss function and the sine function is proposed. By optimizing the inertia weight and speed coefficient of the PSO algorithm, the optimization speed and precision of the algorithm are improved. And improved algorithm is applied to the identification of the system, and the identification results are compared with the existing BPSO and the WPSO. Finally, it is concluded that the improved particle swarm optimization algorithm can get better results than other PSO.