New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks

被引：10
|
作者：
Wang, Wentao [1 ]
Gong, Shuhua [2 ]
Chen, Wei [3 ]
机构：
[1] Shanghai Univ Engn Sci, Sch Math Phys & Stat, Shanghai, Peoples R China
[2] Jiaxing Univ, Coll Math Phys & Informat Engn, Jiaxing, Peoples R China
[3] Shanghai Lixin Univ Accounting & Finance, Sch Stat & Math, Shanghai, Peoples R China
来源：
SYSTEMS SCIENCE & CONTROL ENGINEERING | 2018年 / 6卷 / 01期
关键词：
Stochastic delayed recurrent neural networks; input-to-state stability; itos formula; Lyapunov-Krasovskii functional;
D O I：
10.1080/21642583.2018.1544512
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we solve the mean-square exponential input-to-state stability problem for a class of stochastic delayed recurrent neural networks with time-varying coefficients. With the aid of stochastic analysis theory and a Lyapunov-Krasovskii functional, we derive a novel criterion that ensures the given system is mean-square exponentially input-to-state stable. Furthermore, the new criterion generalizes and improves some known results. Finally, two examples and their numerical simulations are provided to demonstrate the theoretical results.
引用
收藏
页码：501 / 509
页数：9
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