Semi‐Global Impulsive Exponential Stability of Stochastic Complex‐Valued Complex Networks With Distributed Delay and Semi‐Markov Jump

ABSTRACT In this article, we focus on how to achieve semi‐global exponential stability of stochastic complex‐valued complex networks with distributed delay and semi‐Markov jump (SCVCNDDSMJ) under impulse control. Considering complex‐valued systems, stochastic disturbance, and semi‐Markov jump, the segment functions in the Banach space are redefined and Driver's derivative for the functionals are redefined basically. Considering complex networks, we construct a global functional by the solution component to the exponentially stabilized continuous time feedback control systems and some quasi‐properties of are derived. Combining impulse differential inequalities and mathematical induction, semi‐global impulsive exponential stability can be obtained when acts on the solution of the impulse control systems. We also provide some semi‐global impulsive exponential stability criteria including the estimates of the average impulse control interval, the upper bound of the impulse control gain, and the expression between the bound of the initial condition and the maximum impulse interval without imposing restrictions on the bound of the initial condition. Moreover, the theoretical results obtained are utilized in a kind of stochastic complex‐valued recurrent neural networks. In the end, the validity of the obtained theoretical results is confirmed by corresponding numerical simulations.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-10-29
DOI
https://doi.org/10.1002/rnc.70262
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Semi‐Global Impulsive Exponential Stability of Stochastic Complex‐Valued Complex Networks With Distributed Delay and Semi‐Markov Jump

Ju H. Park, Zhenhao Zhang, Ning Zhang, Wenxue Li
International Journal of Robust and Nonlinear Control
Neural Networks Stability and Synchronization
article

Semi‐Global Impulsive Exponential Stability of Stochastic Complex‐Valued Complex Networks With Distributed Delay and Semi‐Markov Jump

Ju H. Park, Zhenhao Zhang, Ning Zhang, Wenxue Li
article en

Abstract

ABSTRACT In this article, we focus on how to achieve semi‐global exponential stability of stochastic complex‐valued complex networks with distributed delay and semi‐Markov jump (SCVCNDDSMJ) under impulse control. Considering complex‐valued systems, stochastic disturbance, and semi‐Markov jump, the segment functions in the Banach space are redefined and Driver's derivative for the functionals are redefined basically. Considering complex networks, we construct a global functional by the solution component to the exponentially stabilized continuous time feedback control systems and some quasi‐properties of are derived. Combining impulse differential inequalities and mathematical induction, semi‐global impulsive exponential stability can be obtained when acts on the solution of the impulse control systems. We also provide some semi‐global impulsive exponential stability criteria including the estimates of the average impulse control interval, the upper bound of the impulse control gain, and the expression between the bound of the initial condition and the maximum impulse interval without imposing restrictions on the bound of the initial condition. Moreover, the theoretical results obtained are utilized in a kind of stochastic complex‐valued recurrent neural networks. In the end, the validity of the obtained theoretical results is confirmed by corresponding numerical simulations.

International Journal of Robust and Nonlinear ControlVol. 36(4)
Harbin Institute of Technology (CN), Weihai Science and Technology Bureau (CN), Yeungnam University (KR)
National Research Foundation, National Natural Science Foundation of China, National Research Foundation of Korea, Natural Science Foundation of Tianjin City, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 63%
Neural Networks Stability and Synchronization
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