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Dynamic behavior of a class of neutral-type neural networks with discontinuous activations and time-varying delays

机译:一种具有不连续激活和时变延迟的一类中立式神经网络的动态行为

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摘要

This paper is concerned with a class of neutral-type neural networks with discontinuous activations and time-varying delays. Under the concept of Filippov solution, by applying the differential inclusions and the topological degree theory in set-valued analysis, we employ a novel argument to establish new results on the existence of the periodic solutions for the considered neural networks. After that, we derive some criteria on the uniqueness, global exponential stability of the considered neural networks and convergence of the corresponding autonomous case of the considered neural networks, in terms of nonsmooth analysis theory with Lyapunov-like approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, the results obtained can also be valid. Our results extend previous works on the neutral-type neural networks to the discontinuous cases, some related results in the literature can be enriched and extended. Finally, two typical examples and the corresponding numerical simulations are provided to show the effectiveness and flexibility of the results derived in this paper.
机译:本文涉及一类具有不连续激活和时变延迟的中性类型神经网络。在Filippov解决方案的概念下,通过在设定值分析中应用差分夹杂物和拓扑学位理论,我们采用了一个新的争论来建立新的结果,以确定所考虑的神经网络的定期解决方案。此后,就Lyapunov样方法而言,我们派生了关于神经网络的唯一性,所考虑的神经网络的唯一性,全球指数稳定性和所考虑的神经网络的相应自主情况的融合。不假设不连续神经元激活功能的有界性(或生长条件)和单调性,所得到的结果也可以有效。我们的结果以前型神经网络向不连续病例扩展了先前的作品,有些相关结果在文献中可以富集和延伸。最后,提供了两个典型的例子和相应的数值模拟,以显示本文中得出的结果的有效性和灵活性。

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