Stochastic stability for distributed delay neural networks via augmented Lyapunov-Krasovskii functionals
Stochastic stability for distributed delay neural networks via augmented Lyapunov-Krasovskii functionals
复制标题
通过增强 Lyapunov-Krasovskii 泛函实现分布式延迟神经网络的随机稳定性
DOI:
10.1016/j.amc.2018.05.059
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发表时间:
2018-12
影响因子:
4
通讯作者:
Alsaadi Fuad E.
中科院分区:
文献类型:
--
作者:
陈永刚;王子栋;刘玉荣;Alsaadi Fuad E.
This paper is concerned with the analysis problem for the globally asymptotic stability of a class of stochastic neural networks with finite or infinite distributed delays. By using the delay decomposition idea, a novel augmented Lyapunov–Krasovskii functional containing double and triple integral terms is constructed, based on which and in combination with the Jensen integral inequalities, a less conservative stability condition is established for stochastic neural networks with infinite distributed delay by means of linear matrix inequalities. As for stochastic neural networks with finite distributed delay, the Wirtinger-based integral inequality is further introduced, together with the augmented Lyapunov–Krasovskii functional, to obtain a more effective stability condition. Finally, several numerical examples demonstrate that our proposed conditions improve typical existing ones.
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