具有Wiener过程的马尔可夫跳跃Hopfield神经网络随机稳定性的LMI方法

Sixth International Conference on Intelligent Systems Design and Applications Pub Date : 2006-10-16 DOI:10.1109/ISDA.2006.187

X. Lou, B. Cui

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引用次数: 1

摘要

研究了具有时变时滞和Wiener过程的马尔可夫跳Hopfield神经网络的随机稳定性问题。我们的注意力集中在建立随机稳定性的充分条件，即使系统包含维纳过程。所得结果均以线性矩阵不等式的形式表示。通过两个算例验证了所提方法的有效性

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LMI Approach for Stochastic Stability of Markovian Jumping Hopfield Neural Networks with Wiener Process

This paper deals with the stochastic stability problem for Markovian jumping Hopfield neural networks (MJHNNs) with time-varying delays and Wiener process. Our attention is focused on developing sufficient conditions on stochastic stability, even if the system contains Wiener process. All the obtained results are presented in terms of linear matrix inequality. The efficiency of the proposed results is demonstrated via two numerical examples

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Sixth International Conference on Intelligent Systems Design and Applications

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