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Dynamics reconstruction in the presence of bistability by using reservoir computer

Author

Listed:
  • Ma, Rencai
  • Dai, Qionglin
  • Li, Haihong
  • Yang, Junzhong

Abstract

Coupled chaotic oscillators usually display rich dynamics and undergo complicated bifurcations when the coupling strength changes. Dynamics reconstruction of the coupled system without relying on a model is a difficult problem in the field of nonlinear dynamics, especially in the presence of bistability. Following previous works (PRE 104 (2021) 014205 and 024205), we design a reservoir computer with parameter input channel consisting of the coupling strength and an indicator parameter. The indicator parameter is used to distinguish the possible coexisting dynamical states. Using a ring of coupled Rössler oscillators, we demonstrate the power of the reservoir computer in dynamics reconstruction and predicting the transitions between different dynamical states. We find that a single one reservoir computer is enough to reconstruct the complete bifurcation diagrams of the original coupled system.

Suggested Citation

  • Ma, Rencai & Dai, Qionglin & Li, Haihong & Yang, Junzhong, 2023. "Dynamics reconstruction in the presence of bistability by using reservoir computer," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004241
    DOI: 10.1016/j.chaos.2023.113523
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    References listed on IDEAS

    as
    1. Hu, Wancheng & Zhang, Yibin & Ma, Rencai & Dai, Qionglin & Yang, Junzhong, 2022. "Synchronization between two linearly coupled reservoir computers," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    2. Sanjukta Krishnagopal & Yiannis Aloimonos & Michelle Girvan, 2018. "Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach," Complexity, Hindawi, vol. 2018, pages 1-15, November.
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