Synchronization of coupled neural networks with switching topology: Dynamic event-triggered impulsive control with delays

This paper investigates the synchronization of neural networks with switching topology under dynamic event-triggered impulsive control with delays. First, a general delayed-time impulsive model is adopted, allowing for a more realistic representation of impulsive effects in neural networks. Second, a new dynamic event-triggered impulsive control scheme is designed, which reduces communication and computation costs and avoids Zeno behavior. Finally, less conservative synchronization criteria are derived, enabling the analysis of more general cases involving neural networks with switching topology, dynamic event-triggered impulsive control, and delayed-time impulses. Numerical examples are provided to demonstrate the effectiveness and superiority of the proposed methods.

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Publication Details

Journal
Transactions of the Institute of Measurement and Control
Published
2025-01-10
DOI
https://doi.org/10.1177/01423312241297328
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.49

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article

Synchronization of coupled neural networks with switching topology: Dynamic event-triggered impulsive control with delays

Ruoyu Li, Lulu Li
1 citations
Transactions of the Institute of Measurement and Control
Neural Networks Stability and Synchronization
1.49
article

Synchronization of coupled neural networks with switching topology: Dynamic event-triggered impulsive control with delays

Ruoyu Li, Lulu Li
article en
1 citations

Abstract

This paper investigates the synchronization of neural networks with switching topology under dynamic event-triggered impulsive control with delays. First, a general delayed-time impulsive model is adopted, allowing for a more realistic representation of impulsive effects in neural networks. Second, a new dynamic event-triggered impulsive control scheme is designed, which reduces communication and computation costs and avoids Zeno behavior. Finally, less conservative synchronization criteria are derived, enabling the analysis of more general cases involving neural networks with switching topology, dynamic event-triggered impulsive control, and delayed-time impulses. Numerical examples are provided to demonstrate the effectiveness and superiority of the proposed methods.

Transactions of the Institute of Measurement and Control
Hefei University of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Anhui Province
Openalex Percentile: Top 21%
Neural Networks Stability and Synchronization
1.49
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