Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen-Grossberg neural networks with mixed delays

Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen-Grossberg neural networks with mixed delays
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混合延迟马尔可夫跳跃反应扩散 Cohen-Grossberg 神经网络的延迟依赖鲁棒指数稳定性

DOI:
10.1016/j.jfranklin.2012.04.005
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发表时间:
2012-08
影响因子:
4.1
通讯作者:
Sun, Xi-Qian
Sun, Xi-Qian
中科院分区:
计算机科学3区
文献类型:
--
作者:
Kao, Yong-Gui;Guo, Ji-Feng;Wang, Chang-Hong;Sun, Xi-Qian

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研究了具有马尔可夫跳变参数和混合时滞的反应扩散Cohen-Grossberg神经网络的鲁棒随机指数稳定性。假设参数不确定性是范数有界的。假设时滞是时变的,且属于给定的区间,这意味着区间时变时滞的上下界是可用的。利用线性矩阵不等式(LMI)的形式,建立了具有马尔可夫跳变参数的RDCGNN时滞相关鲁棒指数稳定性判据,并利用Matlab的LMI工具箱进行了验证.数值例子证明了所提出的结果的有效性。
This paper is devoted to investigating the robust stochastic exponential stability for reaction-diffusion Cohen–Grossberg neural networks (RDCGNNs) with Markovian jumping parameters and mixed delays. The parameter uncertainties are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Some criteria for delay-dependent robust exponential stability of RDCGNNs with Markovian jumping parameters are established in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing Matlab LMI toolbox. Numerical examples are provided to demonstrate the efficiency of the proposed results.
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