Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays

Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays
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混合延迟脉冲随机 Cohen-Grossberg 神经网络的指数 p 稳定性

DOI:
10.1016/j.matcom.2008.08.008
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发表时间:
2009
影响因子:
4.6
通讯作者:
Xu, Daoyi
Xu, Daoyi
中科院分区:
数学3区
文献类型:
--
作者:
Wang, Xiaohu;Guo, Qingyi;Xu, Daoyi

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在本文中,我们研究了具有混合延迟的脉冲随机 Cohen-Grossberg 神经网络。通过建立具有混合时滞的L算子微分不等式,并利用M锥的性质和随机分析技术,我们获得了确保具有混合时滞的脉冲随机Cohen-Grossberg神经网络的指数p稳定性的充分条件。这些结果概括了一些先前已知的结果,并消除了对神经网络的一些限制。还讨论了两个例子来说明所获得结果的效率。
In this paper, we study the impulsive stochastic Cohen–Grossberg neural networks with mixed delays. By establishing an L-operator differential inequality with mixed delays and using the properties of M-cone and stochastic analysis technique, we obtain some sufficient conditions ensuring the exponential p-stability of the impulsive stochastic Cohen–Grossberg neural networks with mixed delays. These results generalize a few previous known results and remove some restrictions on the neural networks. Two examples are also discussed to illustrate the efficiency of the obtained results.
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