Globally Exponential Synchronization of Delayed Complex Dynamic Networks With Average Impulsive Delay‐Gain

ABSTRACT In this article, we investigate globally exponential synchronization problems in delayed complex dynamic networks (DCDNs) characterized by both time‐varying impulsive delay and gain (TIDG). Our research is grounded on the Halanay inequality, which serves as the keystone of our analysis. Adopting the method of average impulsive delay‐gain (AIDG), we formulate criteria for globally exponential synchronization dependent on the overall impulsive disturbances. Our criteria reveal the negative effect of AIDG on synchronization, which hinders the synchronization process. Additionally, we refine the concept of average impulsive gain to enhance its applicability. Furthermore, our results demonstrate that even in the simultaneous presence of desynchronizing and synchronizing impulses, along with time‐varying impulsive delays, DCDNs are able to maintain the original synchronization under appropriate conditions, irrespective of whether the average impulsive interval is finite or not. Finally, we validate our theoretical findings by applying them to network examples.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2024-12-04
DOI
https://doi.org/10.1002/rnc.7753
Citations
4
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.40

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article

Globally Exponential Synchronization of Delayed Complex Dynamic Networks With Average Impulsive Delay‐Gain

Yishu Wang, Jianquan Lu, Kangping Gao, Jürgen Kurths
4 citations
International Journal of Robust and Nonlinear Control
Neural Networks Stability and Synchronization
1.40
article

Globally Exponential Synchronization of Delayed Complex Dynamic Networks With Average Impulsive Delay‐Gain

Yishu Wang, Jianquan Lu, Kangping Gao, Jürgen Kurths
article en
4 citations

Abstract

ABSTRACT In this article, we investigate globally exponential synchronization problems in delayed complex dynamic networks (DCDNs) characterized by both time‐varying impulsive delay and gain (TIDG). Our research is grounded on the Halanay inequality, which serves as the keystone of our analysis. Adopting the method of average impulsive delay‐gain (AIDG), we formulate criteria for globally exponential synchronization dependent on the overall impulsive disturbances. Our criteria reveal the negative effect of AIDG on synchronization, which hinders the synchronization process. Additionally, we refine the concept of average impulsive gain to enhance its applicability. Furthermore, our results demonstrate that even in the simultaneous presence of desynchronizing and synchronizing impulses, along with time‐varying impulsive delays, DCDNs are able to maintain the original synchronization under appropriate conditions, irrespective of whether the average impulsive interval is finite or not. Finally, we validate our theoretical findings by applying them to network examples.

International Journal of Robust and Nonlinear ControlVol. 35(5)
Humboldt-Universität zu Berlin (DE), Potsdam Institute for Climate Impact Research (DE), Southeast University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Southeast University
Openalex Percentile: Top 16%
Neural Networks Stability and Synchronization
1.40
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Globally Exponential Synchronization of Delayed Complex Dynamic Networks With Average Impulsive Delay‐Gain — Yishu Wang, Jianquan Lu, et al. · International Journal of Robust and Nonlinear Control (2024) | TGRS Research Map | TGRS