Oppositional backtracking search optimization algorithm for parameter identification of hyperchaotic systems

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• 作者：Jian Lin (1)
  
  1. School of Information ; Zhejiang University of Finance & Economics ; Hangzhou ; 310018 ; Zhejiang ; People鈥檚 Republic of China
  
• 关键词：Backtracking search optimization ; Parameter identification ; Hyperchaotic system ; Opposition ; based learning
• 刊名：Nonlinear Dynamics
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：80
• 期：1-2
• 页码：209-219
• 全文大小：981 KB
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• 刊物类别：Engineering
• 刊物主题：Vibration, Dynamical Systems and Control
  Mechanics
  Mechanical Engineering
  Automotive and Aerospace Engineering and Traffic
  
• 出版者：Springer Netherlands
• ISSN：1573-269X

文摘

Parameter identification is an important issue in nonlinear science and has received increasing interest in the recent years. In this paper, an oppositional backtracking search optimization algorithm is proposed to solve the parameter identification of hyperchaotic system. The backtracking search optimization algorithm provides a new alternative for population-based heuristic search. To increase the diversity of initial population and to accelerate the convergence speed, the opposition-based learning method is employed in the backtracking search optimization algorithm for population initialization as well as for generation jumping. Numerical simulations on several typical hyperchaotic systems are conducted to demonstrate the effectiveness and robustness of the proposed scheme.