| 基于改进粒子群算法的Hammerstein模型辨识 |
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| 引用本文： | 徐小平,钱富才,王峰,刘红艳.基于改进粒子群算法的Hammerstein模型辨识[J].计算机工程,2008,34(14):200-202. |
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| 作者姓名： | 徐小平 钱富才 王峰 刘红艳 |
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| 作者单位： | 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 |
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| 基金项目： | 高等学校博士学科点专项科研项目
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陕西省自然科学基金 |
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| 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。
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| 关 键 词： | 系统辨识 粒子群优化算法 速度变异 Hammerstein模型 |
| 修稿时间： | |
Identification of Hammerstein Model Based on Improved Particle Swarm Optimization Algorithm |
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| XU Xiao-ping,QIAN Fu-cai,WANG Feng,LIU Hong-yan.Identification of Hammerstein Model Based on Improved Particle Swarm Optimization Algorithm[J].Computer Engineering,2008,34(14):200-202. |
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| Authors: | XU Xiao-ping QIAN Fu-cai WANG Feng LIU Hong-yan |
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| Affiliation: | (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) |
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| 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. |
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| Keywords: | system identification particle swarm optimization algorithm velocity mutation Hammerstein model |
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