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
复制
发表时间:
2018-12
影响因子:
4
通讯作者:
Alsaadi Fuad E.
Alsaadi Fuad E.
中科院分区:
数学2区
文献类型:
--
作者:
陈永刚;王子栋;刘玉荣;Alsaadi Fuad E.

文献摘要

参考文献

被引文献

相似文献

研究了一类具有有限或无限分布时滞的随机神经网络的全局渐近稳定性问题。利用时滞分解的思想,构造了一个新的含二重和三重积分项的增广Lyapunov-Krasovskii泛函,在此基础上,结合詹森积分不等式,利用线性矩阵不等式建立了具有无穷分布时滞的随机神经网络的一个较弱保守稳定性条件.对于具有有限分布时滞的随机神经网络,进一步引入基于Wirtinger的积分不等式,结合增广的Lyapunov-Krasovskii泛函,得到了更有效的稳定性条件.最后,几个数值例子表明,我们提出的条件改善了典型的现有的。
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.
一类具有混合时滞和非线性的复杂网络的事件触发状态估计方法
DOI: 10.1109/tcyb.2015.2478860
发表时间: 2016-11
影响因子: 11.8
作者:
Licheng Wang;Zidong Wang;Tingwen Huang;Guoliang Wei
通讯作者: Guoliang Wei
DOI: 10.1016/j.amc.2018.02.029
发表时间: 2018-08
期刊: Appl. Math. Comput.
影响因子: --
作者:
Li Li-Li;Zhen Wang;Yuxia Li;Hao Shen;Junwei Lu
通讯作者: Li Li-Li;Zhen Wang;Yuxia Li;Hao Shen;Junwei Lu
DOI: 10.1109/tnnls.2017.2750708
发表时间: 2018-09
影响因子: 10.4
作者:
Xianming Zhang;Wen-Juan Lin-;Q. Han;Yong He;Min Wu
通讯作者: Xianming Zhang;Wen-Juan Lin-;Q. Han;Yong He;Min Wu
DOI: 10.1016/j.automatica.2014.11.015
发表时间: 2015-02
期刊: Autom.
影响因子: --
作者:
Yonggang Chen;S. Fei;Yong-ming Li
通讯作者: Yonggang Chen;S. Fei;Yong-ming Li
DOI: 10.1016/j.neucom.2017.08.027
发表时间: 2018-01
期刊: Neurocomputing
影响因子: 6
作者:
W. Qian;Yonggang Chen;Yurong Liu;F. Alsaadi
通讯作者: W. Qian;Yonggang Chen;Yurong Liu;F. Alsaadi