Further Results on Input-to-State Stability of Stochastic Cohen-Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays

被引：82

作者：

Chandrasekar, A. ^{[1
]}

Radhika, T. ^{[2
,4
]}

Zhu, Quanxin ^{[3
]}

机构：

[1] Sona Coll Arts & Sci, Dept Math, Salem 636005, Tamil Nadu, India

[2] Muthayammal Engn Coll, Dept Math, Rasipuram 637408, Tamil Nadu, India

[3] Hunan Normal Univ, Sch Math & Stat, MOE LCSM, Changsha 410081, Peoples R China

[4] Chengdu Univ, Sch Elect Informat & Elect Engn, Chengdu 610106, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2022年 / 54卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Input-to-state stability; Cohen-Grossberg BAM neural networks; Probabilistic time-varying delay; Stochastic systems; GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; SYNCHRONIZATION; DISCRETE; SYSTEMS;

D O I：

10.1007/s11063-021-10649-w

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this article, the problem of stochastic Cohen-Grossberg Bidirectional Associative Memory (CGBAM) neural networks with probabilistic time-varying delay is analyzed by input-to-state stability theory. The stochastic variable with Bernoulli distribution gives the information of probabilistic time-varying delay and it is transformed into one with deterministic time-varying delay in the stochastic manner. Further, by constructing a novel Lyapunov-Krasovskii functional and utilizing Ito's and Dynkin's formula with stochastic analysis theory, the sufficient criterion is derived for the input-to-state stability of stochastic CGBAM neural networks. Finally, numerical examples are provided to examine the merits of the given method.

引用

页码：613 / 635

页数：23

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