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

Chuangxia Huang | Zendy; Xinsong Yang | Zendy; Yigang He | Zendy; Lehua Huang | Zendy

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

Author(s) -

Chuangxia Huang,

Xinsong Yang,

Yigang He,

Lehua Huang

Publication year - 2011

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2011/570295

Subject(s) - semimartingale , exponential stability , stability (learning theory) , convergence (economics) , mathematics , recurrent neural network , reaction–diffusion system , mean square , diffusion , artificial neural network , computer science , mathematical analysis , artificial intelligence , physics , nonlinear system , quantum mechanics , machine learning , economics , thermodynamics , economic growth

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

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