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A review on evolution of Lyapunov-Krasovskii function in stability analysis of recurrent neural networks with single time-varying delay

机译:单时差延迟稳定性延迟稳定性分析Lyapunov-Krasovskii功能的综述

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In the stability analysis of recurrent neural networks, one of the tasks is to reduce the conservativeness of the stability criterion. Along this routine, there are two ways to be considered. One is how to construct the Lyapunov-Krasovskii functional (LKF), and the other is how to use mathematical skills to estimate the derivatives of the LKF. The purpose of this paper is to present a brief review on the evolution on the construction of LKF for recurrent neural networks with single time-varying delay. By summarizing the observation, one can find the core elements in the construction of LKF. Moreover, one can find the evolution history on the delay-partitioning and its applications in the construction of LKF.
机译:在经常性神经网络的稳定性分析中,其中一个任务是降低稳定标准的保守性。沿着这个例程,有两种方法可以考虑。一个是如何构建Lyapunov-Krasovskii功能(LKF),另一个是如何使用数学技能来估计LKF的衍生品。本文的目的是浅谈具有单位不同延迟的经常性神经网络建设的演变。通过总结观察,可以找到建造LKF的核心元素。此外,人们可以在LKF建设中找到关于延迟分区的演进史及其应用。

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