Achieving exponential synchronization in nonlinear stochastic complex networks with time-varying delays: A pinning impulsive control strategy

This study investigates exponential synchronization for complex networks with nonlinear coupling structures, stochastic perturbations, and hybrid time-varying delays. The proposed model integrates both internal and coupling-induced time-varying delays. A pinning impulsive control strategy is developed to synchronize the network, which only requires partial node intervention. Based on stochastic analysis and Lyapunov stability theory, we rigorously derive sufficient conditions for exponential convergence. The results reveal that synchronizing the entire network requires only limited impulsive control inputs, significantly reducing control costs. Finally, two numerical examples validate the theoretical framework and demonstrate its practical effectiveness.

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Publication Details

Journal
Franklin Open
Published
2026-01-16
DOI
https://doi.org/10.1016/j.fraope.2026.100487
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Achieving exponential synchronization in nonlinear stochastic complex networks with time-varying delays: A pinning impulsive control strategy

Shunqin Liu, Xiuliang Qiu, L. Li, Wenshui Lin
Franklin Open
Neural Networks Stability and Synchronization
article

Achieving exponential synchronization in nonlinear stochastic complex networks with time-varying delays: A pinning impulsive control strategy

Shunqin Liu, Xiuliang Qiu, L. Li, Wenshui Lin
article en

Abstract

This study investigates exponential synchronization for complex networks with nonlinear coupling structures, stochastic perturbations, and hybrid time-varying delays. The proposed model integrates both internal and coupling-induced time-varying delays. A pinning impulsive control strategy is developed to synchronize the network, which only requires partial node intervention. Based on stochastic analysis and Lyapunov stability theory, we rigorously derive sufficient conditions for exponential convergence. The results reveal that synchronizing the entire network requires only limited impulsive control inputs, significantly reducing control costs. Finally, two numerical examples validate the theoretical framework and demonstrate its practical effectiveness.

Franklin OpenVol. 14
Jimei University (CN), Xiamen University (CN)
Openalex Percentile: Top 88%
Neural Networks Stability and Synchronization
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Achieving exponential synchronization in nonlinear stochastic complex networks with time-varying delays: A pinning impulsive control strategy — Shunqin Liu, Xiuliang Qiu, et al. · Franklin Open (2026) | TGRS Research Map | TGRS