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New delay-dependent stability criteria for uncertain stochastic neural networks with discrete interval and distributed delays

机译:具有离散区间和分布时滞的不确定随机神经网络的新时滞相关稳定性准则

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摘要

This paper studies the globally robustly asymptotical stability in mean square of uncertain stochastic neural networks with discrete interval and distributed time-varying delays. By constructing an augmented Lyapunov-Krasovskii functional, some delay-dependent criteria for the globally robustly asymptotical stability of such systems are formulated in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to illustrate the effectiveness of the obtained results.
机译:本文研究了具有离散区间和分布时变时滞的不确定随机神经网络均方的全局鲁棒渐近稳定性。通过构造增强的Lyapunov-Krasovskii泛函,根据线性矩阵不等式(LMI)制定了此类系统的全局鲁棒渐近稳定性的一些依赖于延迟的准则。最后,提供了两个数值示例来说明所获得结果的有效性。

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