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

Content Provider	Springer Nature Link
Author	Ding, Xiaohua Su, Huan Zhao, Yuwei Li, Wenxue
Copyright Year	2015
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.
Ending Page	2063
Page Count	9
Starting Page	2055
File Format	PDF
ISSN	09410643
e-ISSN	14333058
Journal	Neural Computing and Applications
Issue Number	7
Volume Number	27
Language	English
Publisher	Springer London
Publisher Date	2015-08-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stochastic BAM neural network Data Mining and Knowledge Discovery Equations with impulses Dynamical systems in control Graph theory Network design and communication Exponential stability Computational Science and Engineering Computational Biology/Bioinformatics Time-varying delays Lyapunov–Krasovskii functional Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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