计算机工程 ›› 2008, Vol. 34 ›› Issue (14): 200-202.doi: 10.3969/j.issn.1000-3428.2008.14.071

• 人工智能及识别技术 • 上一篇    下一篇

基于改进粒子群算法的Hammerstein模型辨识

徐小平1,钱富才1,王 峰2,刘红艳1   

  1. (1. 西安理工大学自动化与信息工程学院,西安 710048;2. 西安交通大学理学院,西安 710049)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-07-20 发布日期:2008-07-20

Identification of Hammerstein Model Based on Improved Particle Swarm Optimization Algorithm

XU Xiao-ping1, QIAN Fu-cai1, WANG Feng2, LIU Hong-yan1   

  1. (1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048; 2. School of Science, Xi’an Jiaotong University, Xi’an 710049)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-07-20 Published:2008-07-20

摘要: 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。

关键词: 系统辨识, 粒子群优化算法, 速度变异, Hammerstein模型

Abstract: 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.

Key words: system identification, particle swarm optimization algorithm, velocity mutation, Hammerstein model

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