Parameter Estimation of the MISO Nonlinear System Based on Improved Particle Swarm Optimization
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.
identification Hammerstein model MISO Standard PSO IPSO
Huaike Fan Weixing Lin
Faculty of Information Science and Technology, Ningbo University, Ningbo 315211, China
国际会议
合肥
英文
2563-2567
2011-09-23（万方平台首次上网日期，不代表论文的发表时间）