Mean Square Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Unbounded Distributed Delays

Autor:	Lehua Huang, Yigang He, Chuangxia Huang
Rok vydání:	2010
Předmět:	Lyapunov function Mean square State variable Article Subject Artificial neural network Stochastic process lcsh:Mathematics lcsh:QA1-939 symbols.namesake Nonlinear system Exponential stability Control theory Modeling and Simulation symbols Applied mathematics Stochastic neural network Mathematics
Zdroj:	Discrete Dynamics in Nature and Society, Vol 2010 (2010)
ISSN:	1607-887X 1026-0226
DOI:	10.1155/2010/513218
Popis:	This paper addresses the issue of mean square exponential stability of stochastic Cohen-Grossberg neural networks (SCGNN), whose state variables are described by stochastic nonlinear integrodifferential equations. With the help of Lyapunov function, stochastic analysis technique, and inequality techniques, some novel sufficient conditions on mean square exponential stability for SCGNN are given. Furthermore, we also establish some sufficient conditions for checking exponential stability for Cohen-Grossberg neural networks with unbounded distributed delays.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ccad38a3de631417a92b71cf353a519 https://doi.org/10.1155/2010/513218 Zobrazit plný text záznamu Plný text ve formátu PDF