Exponential Stability of Complex Network Model With State Coupling Driven by Fractional Gaussian Noise

ABSTRACT This paper investigates exponential stability of complex network model driven by fractional Gaussian noise. Firstly, based on the theory of the Hilbert‐Schmidt operator and the analytic semigroups principle, the mild solution for the proposed stochastic complex networks is given. In addition, the existence and uniqueness of the solution is proved using the contraction map theory. The sufficiency criterion for the global exponential stability condition of the p‐th moment is obtained by the stochastic analysis method. Finally, the effectiveness of the obtained results is verified by providing two types of complex network models with different dimensions, which further demonstrates that the decrease in the Hurst exponent will promote the system to be faster stability.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-08-08
DOI
https://doi.org/10.1002/mma.70047
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Exponential Stability of Complex Network Model With State Coupling Driven by Fractional Gaussian Noise

Xinyu Bai, Shaojuan Ma
Mathematical Methods in the Applied Sciences
Neural Networks Stability and Synchronization
article

Exponential Stability of Complex Network Model With State Coupling Driven by Fractional Gaussian Noise

Xinyu Bai, Shaojuan Ma
article en

Abstract

ABSTRACT This paper investigates exponential stability of complex network model driven by fractional Gaussian noise. Firstly, based on the theory of the Hilbert‐Schmidt operator and the analytic semigroups principle, the mild solution for the proposed stochastic complex networks is given. In addition, the existence and uniqueness of the solution is proved using the contraction map theory. The sufficiency criterion for the global exponential stability condition of the p‐th moment is obtained by the stochastic analysis method. Finally, the effectiveness of the obtained results is verified by providing two types of complex network models with different dimensions, which further demonstrates that the decrease in the Hurst exponent will promote the system to be faster stability.

Mathematical Methods in the Applied SciencesVol. 48(17)
Ningxia University (CN), North Minzu University (CN)
National Natural Science Foundation of China, Graduate Innovation Project of North Minzu University
Openalex Percentile: Top 77%
Neural Networks Stability and Synchronization
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Exponential Stability of Complex Network Model With State Coupling Driven by Fractional Gaussian Noise — Xinyu Bai, Shaojuan Ma · Mathematical Methods in the Applied Sciences (2025) | TGRS Research Map | TGRS