Stability for Neural Networks With Time-Varying Delays via Some New Approaches

Title
Stability for Neural Networks With Time-Varying Delays via Some New Approaches
Author(s)
박주현O.M. Kwon[O.M. Kwon]M.J. Park[M.J. Park]S.M. Lee[S.M. Lee]E.J. Cha[E.J. Cha]
Keywords
EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; LKF APPROACH; CRITERIA; DISCRETE; SYSTEMS; PASSIVITY; DYNAMICS
Issue Date
201302
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.24, no.2, pp.181 - 193
Abstract
This paper considers the problem of delay-dependent stability criteria for neural networks with time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative stability criterion is established in terms of linear matrix inequalities. Second, by proposing novel activation function conditions which have not been proposed so far, further improved stability criteria are proposed. Finally, three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea.
URI
http://hdl.handle.net/YU.REPOSITORY/26513http://dx.doi.org/10.1109/TNNLS.2012.2224883
ISSN
2162-237X
Appears in Collections:
공과대학 > 전기공학과 > Articles
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