Parameter Identification of Chaotic Systems by a Novel Dual Particle Swarm Optimization

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Author(s)

  • Yunxiang Jiang
  • Francis C. M. Lau
  • Shiyuan Wang
  • Chi K. Tse

Detail(s)

Original languageEnglish
Article number1650024
Journal / PublicationInternational Journal of Bifurcation and Chaos
Volume26
Issue number2
Publication statusPublished - Feb 2016
Externally publishedYes

Abstract

In this paper, we propose a dual particle swarm optimization (PSO) algorithm for parameter identification of chaotic systems. We also consider altering the search range of individual particles adaptively according to their objective function value. We consider both noiseless and noisy channels between the original system and the estimation system. Finally, we verify the effectiveness of the proposed dual PSO method by estimating the parameters of the Lorenz system using two different data acquisition schemes. Simulation results show that the proposed method always outperforms the traditional PSO algorithm.

Research Area(s)

  • adaptive search range, chaotic systems, dual particle swarm optimization, Parameter identification