Stability of Stochastic Reaction-Diffusion Recurrent Neural Networks with Unbounded Distributed Delays

Autor:	Yigang He, Lehua Huang, Chuangxia Huang, Xinsong Yang
Rok vydání:	2011
Předmět:	Lyapunov function Article Subject lcsh:Mathematics lcsh:QA1-939 Stability (probability) symbols.namesake Recurrent neural network Semimartingale Exponential stability Control theory Modeling and Simulation Reaction–diffusion system Convergence (routing) symbols Equilibrium solution Mathematics
Zdroj:	Discrete Dynamics in Nature and Society, Vol 2011 (2011)
ISSN:	1607-887X 1026-0226
DOI:	10.1155/2011/570295
Popis:	Stability of reaction-diffusion recurrent neural networks (RNNs) with continuously distributed delays and stochastic influence are considered. Some new sufficient conditions to guarantee the almost sure exponential stability and mean square exponential stability of an equilibrium solution are obtained, respectively. Lyapunov's functional method, M-matrix properties, some inequality technique, and nonnegative semimartingale convergence theorem are used in our approach. The obtained conclusions improve some published results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7bfd021dd0d469a188c3093451484b2b https://doi.org/10.1155/2011/570295 Zobrazit plný text záznamu Plný text ve formátu PDF