Global exponential convergence of non-autonomous cellular neural networks with multi-proportional delays
Global exponential convergence of non-autonomous cellular neural networks with multi-proportional delays
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DOI：
10.1016/j.neucom.2016.01.046
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发表时间：
2016-05-26
期刊：
影响因子：
6
通讯作者：
Liu, Bingwen
中科院分区：
文献类型：
--
作者：
Liu, Bingwen
研究了一类多比例时滞非自治细胞神经网络的指数收敛性。利用微分不等式技巧，我们建立了一个新的结果，以确保所有的解决方案的解决方案，以指数收敛到零向量。我们的研究结果与最近的一些补充。最后给出了一个算例及其数值模拟结果，以验证所得结果的有效性。(C)© 2016 Elsevier B.V.版权所有。
The paper is concerned with the exponential convergence for a class of non-autonomous cellular neural networks with multi-proportional delays. By employing the differential inequality techniques, we establish a novel result to ensure that all solutions of the addressed system converge exponentially to zero vector. Our results complement with some recent ones. Moreover, an illustrative example and its numerical simulation are given to demonstrate the effectiveness of the obtained results. (C) 2016 Elsevier B.V. All rights reserved.