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Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion

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  • Chen, Wei
  • Yu, Yongguang
  • Hai, Xudong
  • Ren, Guojian

Abstract

In this paper, the quasi-synchronization problem of heterogeneous fractional-order coupled neural networks (HFCNNs) is studied. In addition, we also take the spatial diffusion effect into consideration, and design an adaptive controller to attenuate the interference of heterogeneous terms. On the one hand, for quasi-synchronization, we propose a nonlinear distributed control law based on local information exchange between neighboring nodes, so that the synchronization error converges to a regulable bounded domain with a certain decay rate. On the other hand, leader-following quasi-synchronization, the reference trajectory is designed in advance and the corresponding distributed controller is developed to make the synchronization errors still tending to the bounded set. Finally, the simulation results show that the theoretical results are correct.

Suggested Citation

  • Chen, Wei & Yu, Yongguang & Hai, Xudong & Ren, Guojian, 2022. "Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    • Handle: RePEc:eee:apmaco:v:427:y:2022:i:c:s009630032200220x
    DOI: 10.1016/j.amc.2022.127145
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    Cited by:

    1. 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).
    2. 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.
    3. Cui, Xueke & Li, Hong-Li & Zhang, Long & Hu, Cheng & Bao, Haibo, 2023. "Complete synchronization for discrete-time fractional-order coupled neural networks with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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