论文标题
通用差方程的周期性解决方案的存在和稳定性，并应用于泄漏项的延迟神经网络
Existence and stability of a periodic solution of a general difference equation with applications to neural networks with a delay in the leakage terms
论文作者
论文摘要
在本文中，使用诱导参数获得了一般的多维延迟差方程，获得了新的全局指数稳定性标准。在差异方程是周期性的情况下，我们通过构造一种庞加莱地图证明了周期性解决方案的存在。结果用于获得渗漏项延迟的一般离散时间神经网络模型的稳定标准。作为特定情况，我们获得了最近在文献中研究的神经网络模型的新稳定标准，特别是针对低阶和高阶Hopfield和双向关联记忆（BAM）。
In this paper, a new global exponential stability criterion is obtained for a general multidimensional delay difference equation using induction arguments. In the cases that the difference equation is periodic, we prove the existence of a periodic solution by constructing a type of Poincaré map. The results are used to obtain stability criteria for a general discrete-time neural network model with a delay in the leakage terms. As particular cases, we obtain new stability criteria for neural network models recently studied in the literature, in particular for low-order and high-order Hopfield and Bidirectional Associative Memory(BAM).
