Delay-dependent robust dissipativity conditions for delayed neural networks with random uncertainties

Title
Delay-dependent robust dissipativity conditions for delayed neural networks with random uncertainties
Author(s)
박주현J. Wang[J. Wang]H. Shen[H. Shen]J. Wang[J. Wang]
Keywords
TIME-VARYING DELAYS; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; DYNAMICAL-SYSTEMS; DISCRETE; PASSIVITY
Issue Date
201309
Publisher
ELSEVIER SCIENCE INC
Citation
APPLIED MATHEMATICS AND COMPUTATION, v.221, pp.710 - 719
Abstract
This paper deals with the problem of the robust dissipativity analysis for delayed neural networks with randomly occurring uncertainties. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli distributed white noise sequences. By using reciprocally convex approach combined with an extended Wirtinger inequality, some delay-dependent conditions for the concerned neural networks to be stochastically strictly (Q,S,R)-theta-dissipative are established. Finally, two numerical examples are given to illustrate the reduced conservatism and effectiveness of our proposed approach. (C) 2013 Elsevier Inc. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/28970http://dx.doi.org/10.1016/j.amc.2013.07.017
ISSN
0096-3003
Appears in Collections:
공과대학 > 전기공학과 > Articles
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