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Existence and exponential stability of periodic solutions for a class of Cohen-Grossberg neural networks with bounded and unbounded delays

机译:一类有界和无界时滞的Cohen-Grossberg神经网络周期解的存在性和指数稳定性

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This paper is concerned with existence and global exponential stability of periodic solutions for a class of Cohen-Grossberg neural networks with bounded and unbounded delays. By the continuation theorem of coincidence degree theory and differential inequality techniques, we deduce some sufficient conditions ensuring existence as well as global exponential stability of periodic solution. These conditions in our results are milder and less restrictive than that of previous known criteria since the hypothesis of boundedness and differentiability on the activation function are dropped. The theoretical analysis are verified by numerical simulations.
机译:本文涉及一类有界和无界时滞的Cohen-Grossberg神经网络周期解的存在性和全局指数稳定性。通过重合度理论的持续性定理和微分不等式技术,我们推导出了一定条件,保证了周期解的存在性和全局指数稳定性。我们的结果中的这些条件比以前的已知标准更温和,限制更少,因为激活函数的有界性和可微性假设被放弃了。通过数值模拟验证了理论分析。

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