Exponential synchronization of reaction-diffusion neural networks via intermittent event-triggered control

Shuo Huang; Wenxuan Zhou; Zhonggang Hu; Chengjie Xu; Shuo Huang; Wenxuan Zhou; Zhonggang Hu; Chengjie Xu

doi:10.3934/era.2026383

Electronic Research Archive

2026, Volume 34, Issue 12: 9094-9117. doi: 10.3934/era.2026383

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Research article

Exponential synchronization of reaction-diffusion neural networks via intermittent event-triggered control

1.
School of Science, Hunan University of Technology, Zhuzhou 412007, China
2.
School of Science, Nanchang Institute of Science and Technology, Nanchang 330100, China

Received: 29 August 2026 Revised: 23 September 2026 Accepted: 28 September 2026 Published: 26 October 2026

This work investigated the exponential synchronization (ES) of reaction-diffusion neural networks (RDNNs) via a novel aperiodic intermittent event-triggered control strategy. The proposed scheme enables each network node to independently configure its event-triggered threshold, control interval, and rest interval, thereby reducing unnecessary response-state sampling/transmission and controller updates under the assumption that the drive state is locally available. By constructing a piecewise Lyapunov functional and adopting Dini derivative analysis and graph-theory techniques, sufficient conditions for exponential synchronization were established. Furthermore, the exclusion of Zeno behavior was strictly proven to guarantee the feasibility of the designed event-triggered mechanism. Numerical simulations on a 10-node directed RDNN with fully heterogeneous node-wise configurations and 200 Monte Carlo runs validated the effectiveness and advantage of the proposed algorithm in reducing response-state sampling and transmission as well as controller updates compared with event-triggered synchronous schemes.
- aperiodic intermittent control,
- event-triggered control,
- exponential synchronization,
- reaction-diffusion neural networks
Citation: Shuo Huang, Wenxuan Zhou, Zhonggang Hu, Chengjie Xu. Exponential synchronization of reaction-diffusion neural networks via intermittent event-triggered control[J]. Electronic Research Archive, 2026, 34(12): 9094-9117. doi: 10.3934/era.2026383

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Abstract

This work investigated the exponential synchronization (ES) of reaction-diffusion neural networks (RDNNs) via a novel aperiodic intermittent event-triggered control strategy. The proposed scheme enables each network node to independently configure its event-triggered threshold, control interval, and rest interval, thereby reducing unnecessary response-state sampling/transmission and controller updates under the assumption that the drive state is locally available. By constructing a piecewise Lyapunov functional and adopting Dini derivative analysis and graph-theory techniques, sufficient conditions for exponential synchronization were established. Furthermore, the exclusion of Zeno behavior was strictly proven to guarantee the feasibility of the designed event-triggered mechanism. Numerical simulations on a 10-node directed RDNN with fully heterogeneous node-wise configurations and 200 Monte Carlo runs validated the effectiveness and advantage of the proposed algorithm in reducing response-state sampling and transmission as well as controller updates compared with event-triggered synchronous schemes.

References

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