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Combination synchronization and stability analysis of time-varying complex-valued neural networks

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Listed:
  • Hao, Zhang
  • Xing-yuan, Wang
  • Peng-fei, Yan
  • Yu-jie, Sun

Abstract

In this paper, the combination synchronization of time-varying complex-valued neural networks and parameter identification for unknown neural network topology are studied. Combination synchronization is a special synchronization for complex-valued system because real-part and imaginary-part make the neural state more complex than real system and the combination behaviors are more flexible. In addition, in the synchronization process, time-varying delays and unknown parameters are taken into consideration to make the network synchronization more practical and universal. In order to get the synchronization and identification, adaptive feedback control method is utilized and the adaptive combination synchronization controllers are proved with the Lyapunov stability theory. In the simulation, examples are given to demonstrate the robustness and effectiveness of the provided combination synchronizations.

Suggested Citation

  • Hao, Zhang & Xing-yuan, Wang & Peng-fei, Yan & Yu-jie, Sun, 2020. "Combination synchronization and stability analysis of time-varying complex-valued neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:chsofr:v:131:y:2020:i:c:s096007791930431x
    DOI: 10.1016/j.chaos.2019.109485
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    References listed on IDEAS

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    Cited by:

    1. Xiao, Feng & Xie, Lingyun & Wei, Bo, 2022. "Explosive synchronization of weighted mobile oscillators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Baluni, Sapna & Sehgal, Ishani & Yadav, Vijay K. & Das, Subir, 2024. "Exponential synchronization of a class of quaternion-valued neural network with time-varying delays: A Matrix Measure Approach," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Kumar, Ankit & Das, Subir & Yadav, Vijay K. & Rajeev,, 2021. "Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Fang, Qi & Wang, Mingzhu & Li, Xiaodi, 2023. "Event-triggered distributed delayed impulsive control for nonlinear systems with applications to complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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