Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays
Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays
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DOI：
10.1016/j.neucom.2014.09.001
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发表时间：
2015-02
期刊：
影响因子：
6
通讯作者：
M. Ali
中科院分区：
文献类型：
--
作者：
M. Ali
研究了离散分布时滞马尔可夫跳变递归神经网络（MJRNN）的全局稳定性。利用李雅普诺夫泛函理论，得到了一个新的基于线性矩阵不等式（LMI）的稳定性判据，以保证具有离散和分布时滞的马尔可夫跳变递归神经网络的渐近稳定性.利用李雅普诺夫方法和一些不等式技巧，得到了时滞神经网络稳定的几个充分条件。最后通过数值算例验证了理论结果的正确性。
In this paper, global stability of Markovian jumping recurrent neural networks with discrete and distributed delays (MJRNN) is considered. A novel linear matrix inequality (LMI) based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of Markovian jumping recurrent neural networks with discrete and distributed delays. By applying Lyapunov method and some inequality techniques, several sufficient conditions are obtained under which the delayed neural networks are stable. Finally, numerical examples are given to demonstrate the correctness of the theoretical results.