Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay

Peng Shi-you; Peng Shi-guo

doi:10.12052/gdutxb.150151

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Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay

更新时间：2026-05-27

- Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay
- Vol. 34, Issue 1, Pages: 78-83,89(2017)
- 作者机构：
- 作者简介：
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- DOI：10.12052/gdutxb.150151
  CLC： TP273
- Online First：09 January 2017，
  
  Published：2017
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彭诗友, 彭世国. 不确定时滞神经网络的无源性分析[J]. 广东工业大学学报, 2017, 34(1): 78-83,89.

Peng Shi-you, Peng Shi-guo. Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay[J]. 2017, 34(1): 78-83,89.
彭诗友, 彭世国. 不确定时滞神经网络的无源性分析[J]. 广东工业大学学报, 2017, 34(1): 78-83,89. DOI： 10.12052/gdutxb.150151.

Peng Shi-you, Peng Shi-guo. Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay[J]. 2017, 34(1): 78-83,89. DOI： 10.12052/gdutxb.150151.

摘要

对神经网络系统的无源性进行了研究，根据无源性网络理论，得出神经网络满足无源保守性更弱的条件.另外研究了参数不确定时滞神经网络的鲁棒无源性.根据李雅普诺夫稳定性理论、Jensen不等式、Schur补及自由权矩阵等方法，研究表明，从构造一个新颖李雅普诺夫泛函并化简李雅普诺夫泛函导数的二次积分项，能够得到神经网络保守性更小的无源条件.同时，得到了满足神经网络无源条件的李雅普诺夫泛函的所有二次项对称矩阵可非正定.实验仿真证明了本文方法的有效性.

Abstract

The passivity of neural networks of neural network systems is studied. Based on the passive network theory

it the less conservative conditions of the neural network can be obtained. Additionally

the robust passivity of the parameters uncertainties' neural networks with time-delay is analyzed. According to theories and approaches of Lyapunov stability

Jensen integral inequality

Schur complement lemma and free weighting matrices

the research shows that the less conservative passive condition of the neural network can be obtained from constructing a new Lyapunov-Krasovskii functional and simplifying the quadratic terms of the Lyapunov-Krasovskii functional's inverse. Meanwhile

the passivity condition is obtained not requiring all the symmetric involved in the employed quadratic Lyapunov-Krasovskii functional matrices to be positive definite. The results show that the method is effective.

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references

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