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
中科院分区:
文献类型:
--
作者:
Zhong, Shouming;Zhou, Nan;Shi, Kaibo;Cheng, Jun
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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