Stability analysis of memristor-based complex-valued recurrent neural networks with time delays
Stability analysis of memristor-based complex-valued recurrent neural networks with time delays
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DOI：
10.1002/cplx.21618
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发表时间：
2016-03
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影响因子：
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通讯作者：
R. Rakkiyappan;G. Velmurugan;Fathalla A. Rihan;S. Lakshmanan
中科院分区：
文献类型：
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作者：
R. Rakkiyappan;G. Velmurugan;Fathalla A. Rihan;S. Lakshmanan
本文讨论了一类基于忆阻器的具有时延的复值递归神经网络（MCVNN）的稳定性分析。利用局部抑制方法，得到了保证MCVNN在一个紧集上全局吸引所有轨迹的有界性的充分条件。利用Lyapunov-Krasovskii泛函，利用局部不变集和线性矩阵不等式，得到了MCVNN指数稳定和全局稳定的充分条件.本文的分析结果是基于Filippov提出的右端不连续微分方程理论的结果。最后，两个数值例子也显示了我们的理论结果的有效性和实用性。© 2014 Wiley Periodicals，Inc.复杂性，2014
This article addresses stability analysis of a general class of memristor-based complex-valued recurrent neural networks (MCVNNs) with time delays. Some sufficient conditions to guarantee the boundedness on a compact set that globally attracts all trajectories of the MCVNNs are obtained by utilizing local inhibition. Moreover, some sufficient conditions for exponential stability and the global stability of the MCVNNs are established with the help of local invariant sets and linear matrix inequalities using Lyapunov–Krasovskii functional. The analysis results in the article, based on the results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. Finally, two numerical examples are also presented to show the effectiveness and usefulness of our theoretical results. © 2014 Wiley Periodicals, Inc. Complexity, 2014