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首页> 外文期刊>Journal of the Franklin Institute >Enhanced stability criteria of neural networks with time-varying delays via a generalized free-weighting matrix integral inequality
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Enhanced stability criteria of neural networks with time-varying delays via a generalized free-weighting matrix integral inequality

机译：通过广义自由加权矩阵积分不等式增强时变时滞神经网络的稳定性准则

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摘要

This paper deals with the problem of delay-dependent stability analysis for neural networks with time-varying delays. First, by constructing an augmented Lyapunov-Krasovskii functional and utilizing a generalized free-weighting matrix integral inequality, an improved stability criterion for the concerned network is derived in terms of linear matrix inequalities. Second, by considering a marginal augmented vector and modifying a Lyapunov-Krasovsii functional, a further enhanced stability criterion is presented. Third, a less conservative stability condition in which a relaxed inequality related to activation functions is added is introduced. Finally, three numerical examples are included to illustrate the advantage and validity of the proposed criteria. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译：本文研究时变时滞神经网络的时滞相关稳定性分析问题。首先，通过构造一个增强的Lyapunov-Krasovskii泛函并利用广义的自由加权矩阵积分不等式，根据线性矩阵不等式推导了相关网络的改进稳定性准则。其次，通过考虑边际扩充向量并修改Lyapunov-Krasovsii泛函，提出了进一步增强的稳定性准则。第三，引入了较保守的稳定性条件，其中添加了与激活函数有关的松弛不等式。最后，包括三个数值示例，以说明所提出标准的优势和有效性。 （C）2018富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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