Observer-based event-triggered impulsive control of delayed reaction-diffusion neural networks

Luyao Li; Licheng Fang; Huan Liang; Tengda Wei; Luyao Li; Licheng Fang; Huan Liang; Tengda Wei

doi:10.3934/mbe.2025060

Mathematical Biosciences and Engineering

2025, Volume 22, Issue 7: 1634-1652. doi: 10.3934/mbe.2025060

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Research article

Observer-based event-triggered impulsive control of delayed reaction-diffusion neural networks

1.
School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China
2.
School of Mathematics and Statistics, Shandong Normal University, Jinan 250358, China
3.
Shandong Provincial Engineering Research Center of System Control and Intelligent Technology, Shandong Normal University, Jinan 250358, China

Received: 20 March 2025 Revised: 14 April 2025 Accepted: 09 May 2025 Published: 20 May 2025

In this paper, we present a novel design of an observer-based event-triggered impulsive control strategy for delayed reaction-diffusion neural networks subject to impulsive perturbation. The impulsive instants of impulsive control are determined in an event-triggered way, and the control strength is designed by the sampling output of an impulsive observer. Several criteria with Lyapunov conditions and linear matrix inequalities are established for the global exponential stability of delayed reaction-diffusion neural networks. It inherits the advantages of event-triggered impulsive control such as low triggering frequency and high efficiency, and is applicable for networks with unmeasurable states. Finally, the effectiveness of theoretical results is verified by a numerical example.
- event-triggered impulsive control,
- impulsive observer,
- neural networks,
- reaction-diffusion
Citation: Luyao Li, Licheng Fang, Huan Liang, Tengda Wei. Observer-based event-triggered impulsive control of delayed reaction-diffusion neural networks[J]. Mathematical Biosciences and Engineering, 2025, 22(7): 1634-1652. doi: 10.3934/mbe.2025060

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Abstract

In this paper, we present a novel design of an observer-based event-triggered impulsive control strategy for delayed reaction-diffusion neural networks subject to impulsive perturbation. The impulsive instants of impulsive control are determined in an event-triggered way, and the control strength is designed by the sampling output of an impulsive observer. Several criteria with Lyapunov conditions and linear matrix inequalities are established for the global exponential stability of delayed reaction-diffusion neural networks. It inherits the advantages of event-triggered impulsive control such as low triggering frequency and high efficiency, and is applicable for networks with unmeasurable states. Finally, the effectiveness of theoretical results is verified by a numerical example.

References

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