Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays
Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays
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DOI：
10.1109/tnn.2006.881488
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发表时间：
2006-11
影响因子：
--
通讯作者：
Jinde Cao;Kun Yuan;Han-Xiong Li
中科院分区：
文献类型：
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作者：
Jinde Cao;Kun Yuan;Han-Xiong Li
利用Lyapunov-Krasovskii泛函和线性矩阵不等式(LMI)方法，研究了具有离散时变时滞和分布时变时滞的递归神经网络的全局渐近稳定性问题。给出了混合时变时滞递归神经网络全局渐近稳定的几个充分条件。所提出的LMI结果在计算上是高效的，因为它可以使用标准的商业软件进行数值求解。文中给出了两个实例，说明了结果的有效性
By employing the Lyapunov-Krasovskii functional and linear matrix inequality (LMI) approach, the problem of global asymptotical stability is studied for recurrent neural networks with both discrete time-varying delays and distributed time-varying delays. Some sufficient conditions are given for checking the global asymptotical stability of recurrent neural networks with mixed time-varying delay. The proposed LMI result is computationally efficient as it can be solved numerically using standard commercial software. Two examples are given to show the usefulness of the results