Journal
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
Volume 86, Issue 12, Pages 2225-2235Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207160903029802
Keywords
quantum-behaved particle swarm optimization; optimization; parameter identification; chaotic system
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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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