时滞忆阻神经网络的Lagrange稳定性  

Lagrange Stability of Memristive Recurrent Neural Networks with Delays

作　　者：殷芳霞 李小林 

机构地区：[1]上海大学数学系,上海

出　　处：《理论数学》2016年第3期272-277,共6页Pure Mathematics

基　　金：国家自然科学基金资助项目10902065的支持。

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.

关 键 词：Lagrange稳定 非光滑分析 线性矩阵不等式 

分 类 号：O1[理学—数学]