Mean-square exponential input-to-state stability for neutral stochastic neural networks with mixed delays
Mean-square exponential input-to-state stability for neutral stochastic neural networks with mixed delays
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DOI：
10.1016/j.neucom.2016.03.048
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发表时间：
2016-09
期刊：
影响因子：
6
通讯作者：
Yinfang Song;Wen Sun;Feng Jiang
中科院分区：
文献类型：
--
作者：
Yinfang Song;Wen Sun;Feng Jiang
研究了一类中立型随机神经网络的输入-状态稳定性问题。我们考虑的随机神经网络既含有中立项又含有混合时滞。利用Lyapunov-Krasovskii泛函方法、随机分析技术和׳S公式，得到了保证被寻址系统均方指数输入状态稳定的若干充分条件。文中给出了两个数值算例，并进行了仿真，验证了所得结果的有效性。
This paper is concerned with the input-to-state stability problem of a class of neutral stochastic neural networks. The stochastic neural networks that we consider contain both neutral terms and mixed delays. By utilizing the Lyapunov–Krasovskii functional method, stochastic analysis techniques and It o^׳ s formula, some sufficient conditions are derived to ensure the mean-square exponential input-to-state stability of the addressed system. Two numerical examples and their simulations are given to illustrate the effectiveness of the derived results.