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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2016

01.10.2016 | Original Article

A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays

verfasst von: Huan Su, Yuwei Zhao, Wenxue Li, Xiaohua Ding

Erschienen in: Neural Computing and Applications | Ausgabe 7/2016

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Abstract

This paper investigates the global exponential stability for a stochastic bidirectional associative memory (BAM) neural network with time-varying delays. Based on the principle of graph theory, a new method for pth moment exponential stability is derived by combining some inequalities, Lyapunov method and stochastic analysis. The obtained criteria have close relations to the topology property of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the theoretical results.
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Metadaten
Titel
A graph-theoretic approach to exponential stability of stochastic BAM neural networks with time-varying delays
verfasst von
Huan Su
Yuwei Zhao
Wenxue Li
Xiaohua Ding
Publikationsdatum
01.10.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2005-x

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