Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization
Gao Fei(高飞)a)†, Li Zhuo-Qiu(李卓球)b), and Tong Heng-Qing(童恒庆)a)
aDepartment of Science, School of Science, Wuhan University of Technology, Wuhan 430070, China; b Department of Engineering and Mechanics, School of Science, Wuhan University of Technology, Wuhan 430070, China
Abstract This paper proposes a novel quantum-behaved particle swarm optimization (NQPSO) for the estimation of chaos' unknown parameters by transforming them into nonlinear functions' optimization. By means of the techniques in the following three aspects: contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles' convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.
Received: 16 April 2007
Revised: 08 October 2007
Accepted manuscript online:
Fund: Project
supported by the National Natural Science Foundation of China (Grant
No 10647141).
Cite this article:
Gao Fei(高飞), Li Zhuo-Qiu(李卓球), and Tong Heng-Qing(童恒庆) Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization 2008 Chin. Phys. B 17 1196
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