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Event-triggered impulsive synchronization of fractional-order coupled neural networks

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  • Tan, Hailian
  • Wu, Jianwei
  • Bao, Haibo

Abstract

The impulsive synchronization of fractional-order coupled neural networks (FOCNNs) via an event-triggered law is investigated in this paper. For the objective of conserving computing resources and decreasing network load, an event-triggered impulsive control (ETIC) mechanism depending on state errors is introduced. The event-triggered controller updates only at impulsive instants, which are defined by some certain triggering conditions and not predetermined. Then, by means of fractional Lyapunov theory, the Kronecker product together with the comparison principle and Laplace transform, sufficient conditions depending on fractional order are obtained to achieve event-triggered impulsive synchronization of FOCNNs. Furthermore, it is also proved that there exists a positive constant less than the time interval between arbitrary two consecutive impulsive instants, which means the Zeno phenomenon is eliminated. At last, a numerical simulation of the typical chaotic system is presented to indicate the feasibility of the developed ETIC mechanism and the correctness of the obtained results.

Suggested Citation

  • Tan, Hailian & Wu, Jianwei & Bao, Haibo, 2022. "Event-triggered impulsive synchronization of fractional-order coupled neural networks," Applied Mathematics and Computation, Elsevier, vol. 429(C).
    • Handle: RePEc:eee:apmaco:v:429:y:2022:i:c:s0096300322003186
    DOI: 10.1016/j.amc.2022.127244
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    References listed on IDEAS

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    Cited by:

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    2. Sun, Yuting & Hu, Cheng & Yu, Juan & Shi, Tingting, 2023. "Synchronization of fractional-order reaction-diffusion neural networks via mixed boundary control," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    3. Zhang, Hai & Chen, Xinbin & Ye, Renyu & Stamova, Ivanka & Cao, Jinde, 2023. "Adaptive quasi-synchronization analysis for Caputo delayed Cohen–Grossberg neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 49-65.
    4. Kiruthika, R. & Krishnasamy, R. & Lakshmanan, S. & Prakash, M. & Manivannan, A., 2023. "Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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