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Event-Triggered Control Based Fixed/Preassigned-Time Synchronization of Memristive BAM Neural Networks with Mixed-Time Delays

Jiashu Gao1, *
Guodong Zhang1
Junhao Hu1
Guici Chen2
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Submitted: 9 May 2025 | Revised: 11 Jun 2025 | Accepted: 13 Jun 2025 | Published: 30 Jun 2025

Abstract

This paper focuses on fixed-time synchronization (FTS) and preassigned-time synchronization (PTS) of bidirectional associative memory memristive neural networks (BAMMNNs) with mixed-time delays via event-triggered control (ETC). Firstly, by using Lyapunov stability theory, fixed/preassigned-time stability lemmas and inequality techniques, results on FTS and PTS of BAMMNNs are derived. Secondly, compared to asymptotic synchronization and finite-time synchronization, the FTS and PTS studied here achieve faster convergence speeds and more precise settling times. Thirdly, the model incorporates state switching, time-varying and distributed delays; specifically, the time delays do not require differentiability, which enhances the generality of the results. Additionally, a segmented ETC strategy is designed to suit the dual-layer structure of BAMMNNs, where control actions are executed based on set triggering conditions, thus significantly reducing information transmission power consumption. Finally, a numerical simulation example is provided to verify the correctness of the results.

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Gao, J., Zhang, G., Hu, J., & Chen, G. (2025). Event-Triggered Control Based Fixed/Preassigned-Time Synchronization of Memristive BAM Neural Networks with Mixed-Time Delays. Applied Mathematics and Statistics, 2(1), 3. https://doi.org/10.53941/ams.2025.100003
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