A System Identification Method Using Particle Swarm Optimization

王峰 1; 2; 邢科义 1; 徐小平 3

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A System Identification Method Using Particle Swarm Optimization

更新时间：2025-12-08

- A System Identification Method Using Particle Swarm Optimization
- Vol. 43, Issue 2, Pages: 116-120(2009)
- 作者机构：
  
  1. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
  2. 西安交通大学理学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP273
- Online First：10 February 2009，
  
  Published：2009
- 稿件说明：
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王峰 1, 2, 邢科义 1, et al. A System Identification Method Using Particle Swarm Optimization[J]. 2009, 43(2): 116-120.
DOI：

王峰 1, 2, 邢科义 1, et al. A System Identification Method Using Particle Swarm Optimization[J]. 2009, 43(2): 116-120. DOI：

摘要

研究了一种基于粒子群优化算法对系统进行辨识的新方法.该方法的基本思想是将典型数学模型相互组合而构成系统模型

即首先将系统结构辨识问题转化为组合优化问题

然后利用粒子群优化算法同时实现系统的结构辨识与参数辨识.为了进一步提高粒子群优化算法的辨识性能

提出了一种改进的粒子群优化算法.仿真结果表明

给出的辨识算法是合理的

虽然扰动对算法的性能以及辨识结果有一定的影响

但利用文中所提出的改进粒子群优化算法仍然可以理想地辨识出系统的结构以及模型的参数

且与已有辨识算法相比更加有效.

Abstract

A new method was developed for system identification via particle swarm optimization(PSO)algorithm. Its essential reason is to employ classical models to transform the system structure identification problem into a combinational problem. A PSO algorithm is then adopted to implement the identification of the system structure and parameters. To enhance the identification performance of the PSO algorithm

an improved particle swarm optimization(IPSO)algorithm is also presented. Following the simulation results

the rationality of this identification algorithm is verified

and the structure of the system and the parameters of the model can be well identified with the presented IPSO algorithm although the performance of the algorithm and the identification efficiency are both affected when disturbance appears.

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references

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