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On joint identification of the feedback parameters for hyperchaotic systems: An optimization-based approach

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  • Li, Nianqiang
  • Pan, Wei
  • Yan, Lianshan
  • Luo, Bin
  • Xu, Mingfeng
  • Jiang, Ning
  • Tang, Yilong

Abstract

We propose an optimization-based scheme for parameter estimation in high-dimensional chaotic systems, and the symbolic time series analysis (STSA) based method is adopted to address the estimation problem. It is shown that, when the system structure and the corresponding time series are known, the STSA-based method works better with respect to the autocorrelation function (ACF) and the mutual information (MI) technologies. Most importantly, the time delay and the feedback strength of two test systems, i.e., the Mackey–Glass system and an external-cavity semiconductor laser system, can be successfully identified using the proposed scheme. To explore the noise immunity, the influence of certain levels of noise on the STSA-based method is tested.

Suggested Citation

  • Li, Nianqiang & Pan, Wei & Yan, Lianshan & Luo, Bin & Xu, Mingfeng & Jiang, Ning & Tang, Yilong, 2011. "On joint identification of the feedback parameters for hyperchaotic systems: An optimization-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 198-207.
    • Handle: RePEc:eee:chsofr:v:44:y:2011:i:4:p:198-207
    DOI: 10.1016/j.chaos.2011.01.009
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    References listed on IDEAS

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    1. Shahverdiev, E.M. & Nuriev, R.A. & Hashimov, R.H. & Shore, K.A., 2006. "Chaos synchronization between the Mackey–Glass systems with multiple time delays," Chaos, Solitons & Fractals, Elsevier, vol. 29(4), pages 854-861.
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    7. Shahverdiev, E.M. & Hashimov, R.H. & Nuriev, R.A. & Hashimova, L.H. & Huseynova, E.M. & Shore, K.A., 2006. "Inverse chaos synchronization in linearly and nonlinearly coupled systems with multiple time-delays," Chaos, Solitons & Fractals, Elsevier, vol. 29(4), pages 838-844.
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    Cited by:

    1. Bezruchko, B.P. & Ponomarenko, V.I. & Smirnov, D.A. & Sysoev, I.V. & Prokhorov, M.D., 2021. "Class-oriented techniques for reconstruction of dynamics from time series," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

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