Parameter estimation for chaotic systems by particle swarm optimization

Qie He, Ling Wang, Bo Liu

Research output: Contribution to journalArticlepeer-review

165 Scopus citations

Abstract

Parameter estimation for chaotic systems is an important issue in nonlinear science and has attracted increasing interests from various research fields, which could be essentially formulated as a multi-dimensional optimization problem. As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications, owing to its simple concept, easy implementation and quick convergence. However, to the best of our knowledge, there is no published work on PSO for estimating parameters of chaotic systems. In this paper, a PSO approach is applied to estimate the parameters of Lorenz system. Numerical simulation and the comparisons demonstrate the effectiveness and robustness of PSO. Moreover, the effect of population size on the optimization performances is investigated as well.

Original languageEnglish (US)
Pages (from-to)654-661
Number of pages8
JournalChaos, Solitons and Fractals
Volume34
Issue number2
DOIs
StatePublished - Oct 2007

Bibliographical note

Funding Information:
This work is supported by both National Natural Science Foundation of China (Grant Nos. 60204008, 60374060 and 60574072) and National 973 Program (Grant No. 2002CB312200).

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