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Multiple dynamics analysis of Lorenz-family systems and the application in signal detection

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  • Huang, Pengfei
  • Chai, Yi
  • Chen, Xiaolong

Abstract

Inspired by the non-autonomous chaos system and the transient chaotic oscillation, based on the chaos systems of the Lorenz family, the system structure, parameters, and orders are adjusted, and the damping oscillation, sustained chaotic oscillation, transient chaotic oscillation, and chaotic oscillation with multiple chaotic trajectories are found. Firstly, the constrain conditions of the system parameters and fractional-orders are derived. Secondly, one external driving force signal and two control parameters are added to the system, different dynamic behaviors of the system under the excitement of different types, features of the external driving force signals are analyzed, simulated, and discussed. The phase diagram, bifurcation diagram, and Lyapunov exponents, etc are adopted for analysis. Finally, based on the adjusted fractional-order nonlinear systems, a kind of signal detection scheme that can be used under a strong noise environment is designed, some key information of the signal to be detected can be acquired. The simulation results show the effectiveness of the designed signal detection scheme.

Suggested Citation

  • Huang, Pengfei & Chai, Yi & Chen, Xiaolong, 2022. "Multiple dynamics analysis of Lorenz-family systems and the application in signal detection," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:chsofr:v:156:y:2022:i:c:s096007792200008x
    DOI: 10.1016/j.chaos.2022.111797
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    Cited by:

    1. Deng, Jiarui & Lao, Huimin & Lyu, Shanxiang, 2023. "Compressed chaotic signal reconstruction based on deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Ding, Dawei & Xu, Xinyue & Yang, Zongli & Zhang, Hongwei & Zhu, Haifei & Liu, Tao, 2024. "Extreme multistability of fractional-order hyperchaotic system based on dual memristors and its implementation," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    3. Lou, Shuting & Deng, Jiarui & Lyu, Shanxiang, 2022. "Chaotic signal denoising based on simplified convolutional denoising auto-encoder," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).

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