Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

• 来源期刊: 中国物理B（英文版）
• 关键词: parameter estimation online chaos system quantum particle swarm optimization
• 年，卷（期）: 2008,（4）
• 页码范围: 1196-1201
• 页数: 6页
• 语种: 英文

摘要

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 axe 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.