Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay

Zhang Zhi-zhong; Peng Shi-guo

doi:10.12052/gdutxb.160010

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Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay

更新时间：2026-05-27

- Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay
- Vol. 34, Issue 4, Pages: 84-88(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.160010
  CLC： TP273
- Online First：09 July 2017，
  
  Published：2017
- 稿件说明：
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张治中, 彭世国. 随机变时滞神经网络的输入状态稳定性[J]. 广东工业大学学报, 2017, 34(4): 84-88.

Zhang Zhi-zhong, Peng Shi-guo. Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay[J]. 2017, 34(4): 84-88.
张治中, 彭世国. 随机变时滞神经网络的输入状态稳定性[J]. 广东工业大学学报, 2017, 34(4): 84-88. DOI： 10.12052/gdutxb.160010.

Zhang Zhi-zhong, Peng Shi-guo. Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay[J]. 2017, 34(4): 84-88. DOI： 10.12052/gdutxb.160010.

摘要

给出了一种新的稳定性判据∶基于线性矩阵不等式（LMI）的输入状态稳定性判据.基于模型变换，通过构造合适的Lyapunov函数，利用随机分析理论、伊藤（It's）公式和一些不等式方法，以线性矩阵不等式形式给出了随机变时滞神经网络的随机输入状态稳定的充分条件.然后通过数值算例说明所提出的方法有较小的保守性，表明所提出方法的有效性.

Abstract

A new stability criterion based on the linear matrix inequality (LMI) input-to-state stability criterion is given. Based on model transformation

input-to-state stability of Stochastic neural networks with Time-Varying Delay is given a sufficient condition in form of linear matrix inequality by constructing appropriate Lyapunov-Krasovskii functional

stochastic analysis theory and It's formula and applying some inequality approach. Then

the proposed method is proven to be less conservative by illustrating with the numerical examples

which shows the effectiveness of the method.

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references

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