Synchronization of Fractional-Order Reaction–Diffusion Neural Networks via ETILC

Xisheng Dai; Yehui Liu; Yanxue Wang; Jianxiang Zhang; Senping Tian

doi:10.3390/fractalfract9120764

,

and

¹

School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China

²

Guangxi Low-Altitude Unmanned Aircraft Key Technologies Engineering Research Center, Liuzhou 545616, China

³

School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China

^*

Author to whom correspondence should be addressed.

Fractal Fract.2025, 9(12), 764;https://doi.org/10.3390/fractalfract9120764

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Abstract

This paper focuses on the synchronization of fractional-order reaction–diffusion neural networks (FORDNN) under sampling event-triggered iterative learning control. A

D^{α}

-type iterative learning protocol of complete synchronization is proposed, which combines the advantages of both event-triggered control and sampling iterative learning control. Using mathematical tools, sufficient conditions for synchronization are derived. A numerical simulation example is provided to confirm the effectiveness of the analysis.

Keywords:

FORDNN; sampling iterative learning control; event-triggered control; synchronization

Synchronization of Fractional-Order Reaction–Diffusion Neural Networks via ETILC

Abstract

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