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.
Lagrange稳定,非光滑分析,线性矩阵不等式, Lagrange Stability Nonsmooth Analysis Linear Matrix Inequality (LMI)时滞忆阻神经网络的Lagrange稳定性
殷芳霞,李小林. 时滞忆阻神经网络的Lagrange稳定性Lagrange Stability of Memristive Recurrent Neural Networks with Delays[J]. 理论数学, 2016, 06(03): 272-277. http://dx.doi.org/10.12677/PM.2016.63041
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<br>http://dx.doi.org/10.1007/978-94-015-7793-9