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Delay-dependent exponential stability for uncertain neutral stochastic neural networks with interval time-varying delay

机译:区间时变时滞不确定中立随机神经网络的时滞依赖指数稳定性

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摘要

This paper is mainly concerned with the problem for the robustly exponential stability in mean square moment of uncertain neutral stochastic neural networks with interval time-varying delay. With an appropriate augmented Lyapunov-Krasovskii functional (LKF) formulated, the convex combination method is utilised to estimate the derivative of the LKF. Some new delay-dependent exponential stability criteria for such systems are obtained in terms of linear matrix inequalities, which involve fewer matrix variables and have less conservatism. Finally, two illustrative numerical examples are given to show the effectiveness of our obtained results.
机译:本文主要研究不确定的具有间隔时变时滞的随机神经网络的均方矩的鲁棒指数稳定性问题。在制定了适当的增强Lyapunov-Krasovskii泛函(LKF)的情况下,利用凸组合方法来估计LKF的导数。根据线性矩阵不等式获得了一些针对此类系统的新的依赖于延迟的指数稳定性准则,线性不等式涉及较少的矩阵变量并且具有较小的保守性。最后,给出了两个说明性的数值示例,以显示我们获得的结果的有效性。

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