Parameters identification of chaotic systems by quantum-behaved particle swarm optimization

被引：23
|
作者：
Yang, Kaiqiao [1 ]
Maginu, Kenjiro [1 ]
Nomura, Hirosato [1 ]
机构：
[1] Kyushu Inst Technol, Dept Artificial Intelligence, Iizuka, Fukuoka 8208502, Japan
来源：
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2009年 / 86卷 / 12期
关键词：
quantum-behaved particle swarm optimization; optimization; parameter identification; chaotic system;
D O I：
10.1080/00207160903029802
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper applies a novel evolutionary optimization algorithm named quantum-behaved particle swarm optimization (QPSO) to estimate the parameters of chaotic systems, which can be formulated as a multimodal numerical optimization problem with high dimension from the viewpoint of optimization. Moreover, in order to improve the performance of QPSO, an adaptive mechanism is introduced for the parameter beta of QPSO. Finally, numerical simulations are provided to show the effectiveness and efficiency of the modified QPSO method.
引用
收藏
页码：2225 / 2235
页数：11
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