Almost sure exponential numerical stability of balanced Maruyama with two step approximations of stochastic time delay Hopfield neural networks
Publish Year: 1403
Type: Journal paper
Language: English
View: 182
This Paper With 13 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
Export:
Document National Code:
JR_CMDE-12-1_011
Index date: 25 November 2023
Almost sure exponential numerical stability of balanced Maruyama with two step approximations of stochastic time delay Hopfield neural networks abstract
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.
Almost sure exponential numerical stability of balanced Maruyama with two step approximations of stochastic time delay Hopfield neural networks Keywords:
Almost sure exponential stability , balanced two step Maruyama numerical approximations , Hopfield neural networks , Stochastic delay differential equations
Almost sure exponential numerical stability of balanced Maruyama with two step approximations of stochastic time delay Hopfield neural networks authors
Sivarajan Kopperundevi
Department of Mathematics, Dr. M.G.R Educational and Research Institute(To be Deemed), Maduravoyal, Chennai-۶۰۰ ۰۹۵, India.