Application of Online System Identification Based on Improved Quantum-behaved Particle Swarm Optimization

被引：3
|
作者：
Zhao, Ji [1 ]
Sun, Jun [1 ]
Xu, Wenbo [1 ]
机构：
[1] JiangNan Univ Informat Sch, Wuxi, Jiangsu, Peoples R China
来源：
SECOND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 2, PROCEEDINGS | 2009年
关键词：
online identification; particle swarm optimization; quantum-behaved particle swarm optimization; single neuron;
D O I：
10.1109/ISCID.2009.194
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Application of online system identification based on improved Quantum-behaved Particle Swarm Optimization is studied in this paper. QPSO algorithm combined with the single neuron can improve the local search capabilities and identification accuracy. Then the improved QPSO is applied to online identify parameters of a system described by differential equations and compared with the improved particle swarm algorithm and genetic algorithm. Simulation results show that improved QPSO algorithm significantly accelerates the online identification. Moreover the convergence speed and accuracy of the improved QPSO is far better than that of the improved PSO and GA algorithm. Time-delay and parameter changes for the simulation experiment illustrates the stability and tracking capability of improved QPSO algorithm are better than the improved PSO algorithm.
引用
收藏
页码：186 / 189
页数：4
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