时滞忆阻神经网络的Lagrange稳定性

摘要

在本文中我们研究了时滞递归忆阻神经网络在Lagrange意义下的全局指数稳定性。通过运用非光滑分析方法、微分包含和不等式技巧[1] [2]，我们得到了新的忆阻神经网络Lagrange稳定的充分条件，同时，我们给出了全局吸引集的估计方法。 In this paper, we study the globally exponential stability in a Lagrange sense for memristive re-current neural networks with time-varying delays. By the results from the theories of nonsmooth analysis, differential inclusions and linear matrix inequalities [1] [2], a novel sufficient criterion in the form of linear matrix inequality is given to confirm the Lagrange stability of memristive re-current neural networks. Meanwhile, the estimation of the globally exponentially attractive set is also given.