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Finite-time stability analysis of fractional-order complex-valued memristor-based neural networks with time delays

机译:基于分数阶复值忆阻器的时滞神经网络的有限时间稳定性分析

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In this paper, the problem of finite-time stability of fractional-order complex-valued memristor-based neural networks (NNs) with time delays is extensively investigated. We first initiate the fractional-order complex-valued memristor-based NNs with the Caputo fractional derivatives. Using the theory of fractional-order differential equations with discontinuous right-hand sides, Laplace transforms, Mittag-Leffler functions and generalized Gronwall inequality, some new sufficient conditions are derived to guarantee the finite-time stability of the considered fractional-order complex-valued memristor-based NNs. In addition, some sufficient conditions are also obtained for the asymptotical stability of fractional-order complex-valued memristor-based NNs. Finally, a numerical example is presented to demonstrate the effectiveness of our theoretical results.
机译:本文研究了具有时滞的分数阶基于复数忆阻器的神经网络(NNs)的有限时间稳定性问题。我们首先使用Caputo分数导数来启动基于分数阶复值忆阻器的NN。使用具有不连续右手边的分数阶微分方程,Laplace变换,Mittag-Leffler函数和广义Gronwall不等式的理论,得出了一些新的充分条件,以保证所考虑的分数阶复数值的有限时间稳定性基于忆阻器的NN。此外,还获得了一些分数阶复值忆阻器为基础的神经网络的渐近稳定性的条件。最后,通过数值例子说明了我们理论结果的有效性。

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