Finite time synchronization for the stochastic complex networks model based on multi-factors influence

This paper mainly studies the finite time synchronization problem of stochastic multi weights complex network model with time-varying delay. First, the multi weights complex network model was established, which includes Lévy noise and coupled time-varying delay. Second, by designing continuous control strategy, the new criterion for finite time synchronization of multi weights complex network model driven by Lévy noise was discussed with inequalities and Lyapunov theory. Finally, the effectiveness of the theoretical results was verified through two numerical examples. The results show that as the connection weights between nodes increase, the interaction between nodes is enhanced, which helps the network reach synchronization faster. Time-varying delay and Lévy noise can lead to the decrease in network synchronization quality, which is caused by increasing synchronization errors or extending synchronization time.

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Publication Details

Journal
Journal of Vibration and Control
Published
2025-12-26
DOI
https://doi.org/10.1177/10775463251410837
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Finite time synchronization for the stochastic complex networks model based on multi-factors influence

Ma Shuo, Ma Shaojuan, Bai Xinyu
Journal of Vibration and Control
Neural Networks Stability and Synchronization
article

Finite time synchronization for the stochastic complex networks model based on multi-factors influence

Ma Shuo, Ma Shaojuan, Bai Xinyu
article en

Abstract

This paper mainly studies the finite time synchronization problem of stochastic multi weights complex network model with time-varying delay. First, the multi weights complex network model was established, which includes Lévy noise and coupled time-varying delay. Second, by designing continuous control strategy, the new criterion for finite time synchronization of multi weights complex network model driven by Lévy noise was discussed with inequalities and Lyapunov theory. Finally, the effectiveness of the theoretical results was verified through two numerical examples. The results show that as the connection weights between nodes increase, the interaction between nodes is enhanced, which helps the network reach synchronization faster. Time-varying delay and Lévy noise can lead to the decrease in network synchronization quality, which is caused by increasing synchronization errors or extending synchronization time.

Journal of Vibration and Control
Ningxia University (CN), North Minzu University (CN)
Openalex Percentile: Top 34%
Neural Networks Stability and Synchronization
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