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Output synchronization for fractional-order reaction–diffusion coupled neural networks with output coupling via event-triggered impulsive control

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  • Bao, Haibo
  • Li, Xuelian

Abstract

In this paper, the output synchronization for a type of fractional-order reaction–diffusion coupled neural networks (FRDCNNs) with output coupling is considered. For the purpose of enhancing effectiveness of the information transmission and saving communication resources, an event-triggered impulsive controller (ETIC) is established, where the impulsive inputs is based on a predetermined event-triggered scheme. Then, based on fractional calculus theory, and choosing appropriate Lyapunov function, the sufficient criterion is established using some inequality techniques to ensure output synchronization of FRDCNNs with output coupling. Furthermore, it is also proved that the Zeno phenomenon is excluded under designed ETIC. Eventually, numerical simulations are presented to verify the viability of newly constructed ETIC and the rightness of acquired theoretical findings.

Suggested Citation

  • Bao, Haibo & Li, Xuelian, 2025. "Output synchronization for fractional-order reaction–diffusion coupled neural networks with output coupling via event-triggered impulsive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    • Handle: RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125003474
    DOI: 10.1016/j.physa.2025.130695
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    References listed on IDEAS

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    1. Zhu, Sha & Bao, Haibo & Cao, Jinde, 2022. "Bipartite synchronization of coupled delayed neural networks with cooperative-competitive interaction via event-triggered control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. 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).
    3. Lu, Jianquan & Cao, Jinde, 2007. "Synchronization-based approach for parameters identification in delayed chaotic neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 672-682.
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