基于改进粒子群算法的Hammerstein模型辨识

Identification of Hammerstein Model Based on Improved Particle Swarm Optimization Algorithm

提出辨识非线性Hammerstein模型的新方法。将非线性系统的辨识问题转化为参数空间上的函数优化问题,采用粒子群算法获得该优化问题的解。为了进一步增强粒子群优化算法的辨识性能,提出采用速度变异粒子群对整个参数空间进行搜索得到系统参数的最优估计。仿真结果验证了该方法的有效性。

This paper presents the system identification method of nonlinear Hammerstein model. The problems of nonlinear system identification are cast as function optimization over parameter space, and the Particle Swarm Optimization（PSO） is adopted to solve the optimization problem. In order to enhance the performance of the PSO identification fatherly, a Velocity Mutation Particle Swarm Optimization（PSOVM） is applied to search the parameter space to find the optimal estimation of the system parameters. Simulation results show the effectiveness of the proposed method.