Global asymptotic stability of nonautonomous Cohen-Grossberg neural network models with infinite delays
Global asymptotic stability of nonautonomous Cohen-Grossberg neural network models with infinite delays
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DOI：
10.1016/j.amc.2015.04.103
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Salete Esteves;José J. Oliveira
中科院分区：
文献类型：
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作者：
Salete Esteves;José J. Oliveira
对于具有潜在无界时变系数和无限分布时滞的一般Cohen-Grossberg神经网络模型，给出了其全局渐近稳定的充分条件。所研究的模型具有足够的通用性，可以将Cohen-Grossberg、Hopfield和双向联想记忆等最著名的神经网络模型作为子类。与通常的文献相反，在证明中我们没有使用李雅普诺夫泛函。如图所示，将结果应用于文献中研究的几个具体模型，结果的比较表明，我们的结果为几个神经网络模型提供了新的全局稳定性准则，并改进了一些先前的出版物。
For a general Cohen–Grossberg neural network model with potentially unbounded time-varying coefficients and infinite distributed delays, we give sufficient conditions for its global asymptotic stability. The model studied is general enough to include, as subclass, the most of famous neural network models such as Cohen–Grossberg, Hopfield, and bidirectional associative memory. Contrary to usual in the literature, in the proofs we do not use Lyapunov functionals. As illustrated, the results are applied to several concrete models studied in the literature and a comparison of results shows that our results give new global stability criteria for several neural network models and improve some earlier publications.