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首页> 外文期刊>Neurocomputing >Existence and global exponential stability of anti-periodic solution for Clifford-valued inertial Cohen-Grossberg neural networks with delays
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Existence and global exponential stability of anti-periodic solution for Clifford-valued inertial Cohen-Grossberg neural networks with delays

机译：Clifford值时滞惯性Cohen-Grossberg神经网络反周期解的存在性和全局指数稳定性

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摘要

In this paper, we are concerned with a class of Clifford-valued inertial Cohen-Grossberg neural networks with time-varying delays. Based on the coincidence degree theory and the Wirtinger inequality, we first obtain sufficient conditions ensuring the existence of anti-periodic solutions for this class of networks. Then, by constructing a suitable Lyapunov functional, we establish the global exponential stability of the anti-periodic solutions. Our results are completely new even if the considered neural networks degenerate into real-valued, complex-valued or quaternion-valued. Finally, we present a numerical example to show the effectiveness of our results (C) 2018 Elsevier B.V. All rights reserved.
机译：在本文中，我们关注一类具有时变时滞的Clifford值惯性Cohen-Grossberg神经网络。基于重合度理论和Wirtinger不等式，我们首先获得足够的条件，以确保此类网络存在反周期解。然后，通过构造合适的Lyapunov函数，我们建立了反周期解的全局指数稳定性。即使考虑的神经网络退化为实值，复值或四元数值，我们的结果也是全新的。最后，我们提供一个数值示例来证明我们的结果（C）2018 Elsevier B.V.的有效性。保留所有权利。

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