Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
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DOI：
10.1109/tnn.2006.872355
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发表时间：
2006-05
影响因子：
--
通讯作者：
Zidong Wang;Yurong Liu;Maozhen Li;Xiaohui Liu
中科院分区：
文献类型：
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作者：
Zidong Wang;Yurong Liu;Maozhen Li;Xiaohui Liu
本文研究了一类具有离散时滞和分布时滞的随机Cohen-Grossberg神经网络的全局渐近稳定性分析问题。基于Lyapunov-Krasovskii泛函和随机稳定性分析理论，利用线性矩阵不等式(LMI)方法，得到了平衡点在均方意义下全局渐近收敛的几个充分条件。证明了具有混合时滞的寻址随机Cohen-Grossberg神经网络在两个LMI可行的情况下在均方意义下是全局渐近稳定的，其中LMI的可行性很容易用MatLab LMI工具箱检验。文中还指出，主要结果包含了一些已有结果作为特例。数值算例验证了所提出的全局稳定性判据的有效性
In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria