H-infinity state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances

Title
H-infinity state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances
Author(s)
박주현M.J. Park[M.J. Park]O.M. Kwon[O.M. Kwon]S.M. Lee[S.M. Lee]E.J. Cha[E.J. Cha]
Keywords
DERIVATIVE-DEPENDENT STABILITY; GLOBAL ASYMPTOTIC STABILITY; DISTRIBUTED DELAY; LINEAR-SYSTEMS; FUZZY-SYSTEMS; CRITERIA; SYNCHRONIZATION; DYNAMICS; MODEL
Issue Date
201504
Publisher
ELSEVIER SCIENCE BV
Citation
NEUROCOMPUTING, v.153, pp.255 - 270
Abstract
This paper considers the problem of delay-dependent H-infinity state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances. By constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative criterion for the existence of the estimator of discrete-time neural networks without disturbances is introduced in Theorem 1 with the framework of linear matrix inequalities (LMIs). Based on the result of Theorem 1, a designing criterion of the estimator for a newly constructed error dynamic system with probabilistic diverging disturbances between original system and estimator will be proposed in Theorem 2. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea. (C) 2014 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/32843http://dx.doi.org/10.1016/j.neucom.2014.11.029
ISSN
0925-2312
Appears in Collections:
공과대학 > 전기공학과 > Articles
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