Stability analysis of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time varying delays
Stability analysis of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time varying delays
复制标题
DOI：
10.1088/1674-1056/23/6/060702
复制
发表时间：
2014-04
影响因子：
1.7
通讯作者：
M. S. Ali
中科院分区：
文献类型：
--
作者：
M. S. Ali
研究了具有离散和分布时变时滞的马尔可夫跳变随机Cohen-Grossberg神经网络（MJSCGNNs）的全局渐近稳定性问题.通过构造一个新的李雅普诺夫泛函，得到了一个新的基于LMI的稳定性判据，以保证MJSCGNNs的渐近稳定性。我们的结果可以很容易地验证，他们也比以前已知的标准限制较少，可以应用到科恩-Grossberg神经网络，递归神经网络和细胞神经网络。最后，通过数值算例验证了所提出的稳定性条件.
In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen—Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples.