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

Yunxiang Jiang, Chung Ming Lau, Shiyuan Wang, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Article number1650024
JournalInternational Journal of Bifurcation and Chaos
Issue number2
Publication statusPublished - 1 Feb 2016


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

ASJC Scopus subject areas

  • Modelling and Simulation
  • Engineering (miscellaneous)
  • General
  • Applied Mathematics


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