理论数学

Vol.6 No.3 (May 2016)

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 :上海大学数学系,上海

 

关键词:

Lagrange稳定非光滑分析线性矩阵不等式Lagrange Stability Nonsmooth Analysis Linear Matrix Inequality (LMI)

 

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.

文章引用:

殷芳霞 , 李小林 (2016) 时滞忆阻神经网络的Lagrange稳定性。 理论数学, 6, 272-277. doi: 10.12677/PM.2016.63041

 

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