Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization
Li Zhuo-Qiua, Gao Feib, Tong Heng-Qingb
a Department of Engineering and Mechanics, School of
Science, Wuhan University of Technology, Wuhan 430070,
China; b Department of Science, 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
Published: 01 April 2008
Fund: Project
supported by the National Natural Science Foundation of China (Grant
No 10647141).
Cite this article:
Gao Fei, Li Zhuo-Qiu, Tong Heng-Qing Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization 2008 Chin. Phys. B 17 1196
