Delay-difference-dependent robust exponential stability for uncertain stochastic neural networks with multiple delays

Title
Delay-difference-dependent robust exponential stability for uncertain stochastic neural networks with multiple delays
Author(s)
박주현J. Xia[J. Xia]H. Zeng[H. Zeng]H. Shen[H. Shen]
Keywords
TIME-VARYING DELAYS; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; TRANSITION-PROBABILITIES; NEUTRAL-TYPE; CRITERIA; INTERVAL; BOUNDEDNESS; SYSTEMS
Issue Date
201409
Publisher
ELSEVIER SCIENCE BV
Citation
NEUROCOMPUTING, v.140, pp.210 - 218
Abstract
This paper deals with the problem of robust exponential stability for a class of uncertain stochastic neural networks with multiple delays. Based on the multiple-difference-dependent Lyapunov-Krasovskii functional and free-weighting matrices method, some novel stability criteria for the addressed uncertain stochastic neural networks are derived. At last, two numerical examples are presented to show the effectiveness and improvement of the proposed results. (C) 2014 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/30804http://dx.doi.org/10.1016/j.neucom.2014.03.022
ISSN
0925-2312
Appears in Collections:
공과대학 > 전기공학과 > Articles
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