期刊
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
卷 86, 期 12, 页码 2225-2235出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207160903029802
关键词
quantum-behaved particle swarm optimization; optimization; parameter identification; chaotic system
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.
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