Exponential stability for stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays

Exponential stability for stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays
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具有离散和分布式时变延迟的随机 Cohen-Grossberg BAM 神经网络的指数稳定性

DOI:
10.1016/j.neucom.2013.08.028
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发表时间:
2014-03
期刊:
影响因子:
6
通讯作者:
Cheng, Jun
Cheng, Jun
中科院分区:
计算机科学2区
文献类型:
--
作者:
Zhong, Shouming;Zhou, Nan;Shi, Kaibo;Cheng, Jun

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研究了具有离散和分布时滞的随机Cohen-Grossberg BAM(SCGBAM)神经网络的指数稳定性分析问题。应用随机分析理论,建立了一个新的Lyapunov-Krasovskii泛函,给出了系统指数稳定的判据。给出了离散时滞和分布时滞随机Cohen-Grossberg BAM神经网络指数稳定的一组新的充分条件。本文提出的几个指数稳定性判据简单有效。最后,给出了两个数值算例,验证了所提结果的低保守性和有效性。
This paper considers the issue of exponential stability analysis for stochastic Cohen–Grossberg BAM (SCGBAM) neural networks with discrete and distributed time-varying delays. The exponential stability criteria are proposed by applying stochastic analysis theory and establishing a new Lyapunov–Krasovskii functional. A set of novel sufficient conditions is obtained to guarantee the exponential stability of stochastic Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. The several exponential stability criteria proposed in this paper are simpler and effective. Finally, two numerical examples are provided to demonstrate the low conservatism and effectiveness of the proposed results.
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