Further Results on Mean-Square Exponential Input-to-State Stability of Stochastic Delayed Cohen-Grossberg Neural Networks

被引：16
|
作者：
Wang, Wentao [1 ]
机构：
[1] Shanghai Univ Engn Sci, Sch Math Phys & Stat, Shanghai 201620, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2023年 / 55卷 / 04期
关键词：
Stochastic Cohen-Grossberg neural networks; Mean-square exponential input-to-state stability; Lipschitz condition; DISCRETE;
D O I：
10.1007/s11063-022-10974-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We consider the global existence of solutions and mean-square exponential input-to-state stability for a class of stochastic delayed Cohen-Grossberg neural networks without global Lipschitz condition. Under local Lipschitz condition, we find new sufficient conditions that ensure the solutions of given neural networks exist globally and are mean-square exponentially input-to-state stable. Furthermore, we highlight the advantages of our novel results by comparing with the results in Zhou et al. (2015) as well as a numerical example.
引用
收藏
页码：3953 / 3965
页数：13
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