脉冲随机 Cohen-Grossberg 神经网络的稳定性与混合时滞和反应扩散项。
The stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays and reaction-diffusion terms.
机构信息
College of Electronic and Information Engineering, Southwest University, Chongqing, 400715 China ; College of Mathematics and Physics, Chongqing University of Science and Technology, Chongqing, 401331 China.
College of Electronic and Information Engineering, Southwest University, Chongqing, 400715 China.
出版信息
Cogn Neurodyn. 2015 Apr;9(2):213-20. doi: 10.1007/s11571-014-9316-y. Epub 2014 Nov 4.
Abstract
The global asymptotic stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays and reaction-diffusion terms is investigated. Under some suitable assumptions and using Lyapunov-Krasovskii functional method, we apply the linear matrix inequality technique to propose some new sufficient conditions for the global asymptotic stability of the addressed model in the stochastic sense. The mixed time delays comprise both the time-varying and continuously distributed delays. The effectiveness of the theoretical result is illustrated by a numerical example.
摘要
研究了具有混合时滞和反应扩散项的脉冲随机 Cohen-Grossberg 神经网络的全局渐近稳定性。在一些合适的假设下，利用 Lyapunov-Krasovskii 泛函方法，我们应用线性矩阵不等式技术，提出了该模型在随机意义下全局渐近稳定性的一些新的充分条件。混合时滞包括时变时滞和连续分布时滞。通过一个数值例子说明了理论结果的有效性。
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