Almost sure exponential numerical stability of balanced Maruyama with two step approxima-tions of stochastic time delay Hopfield neural networks

被引：0
|
作者：
Kopperundevi, Sivarajan [1 ]
机构：
[1] Dr MGR Educ & Res Inst To be Deemed, Dept Math, Chennai 600095, India
来源：
COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS | 2024年 / 12卷 / 01期
关键词：
Almost sure exponential stability; Balanced two step Maruyama numerical approximations; Hopfield neural networks; Stochastic delay differential equations; MEAN-SQUARE STABILITY;
D O I：
10.22034/cmde.2023.55861.2330
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This study examines the balanced Maruyama with two step approximations of stochastic Hopfield neural networks with delay. The main aim of this paper is to discover the conditions under which the exact solutions remain stable for the balanced Maruyama with two-step approximations of stochastic delay Hopfield neural networks (SDHNN). The semi-martingale theorem for convergence is used to demonstrate the almost sure exponential stability of balanced Maruyama with two-step approximations of stochastic delay Hopfield networks. Additionally, the numerical balanced Euler approximation's stability conditions are compared. Our theoretical findings are illustrated with numerical experiments.
引用
收藏
页码：136 / 148
页数：13
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