Full metadata record

DC FieldValueLanguage
dc.contributor.author박주현ko
dc.contributor.authorR. Rakkiyappan[R. Rakkiyappan]ko
dc.contributor.authorN. Sakthivel[N. Sakthivel]ko
dc.contributor.authorO.M. Kwon[O.M. Kwon]ko
dc.date.accessioned2015-12-17T03:05:55Z-
dc.date.available2015-12-17T03:05:55Z-
dc.date.created2015-11-13-
dc.date.issued201309-
dc.identifier.citationAPPLIED MATHEMATICS AND COMPUTATION, v.221, pp.741 - 769-
dc.identifier.issn0096-3003-
dc.identifier.urihttp://hdl.handle.net/YU.REPOSITORY/28963-
dc.identifier.urihttp://dx.doi.org/10.1016/j.amc.2013.07.007-
dc.description.abstractIn this paper, the problem of state estimation for Markovian jumping fuzzy cellular neural networks (FCNNs) using sampled-data with mode-dependent probabilistic time-varying delays is investigated. By developing a delay decomposition approach, the information of the delayed states can be taken into full consideration. By introducing a stochastic variable with a Bernoulli distribution, the information of probability distribution of the time-varying delay is considered and transformed into one with deterministic time-varying delay. The main purpose of this paper is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally asymptotically stable in the mean square. Based on the Lyapunov-Krasovskii functional including triple integral terms and decomposed integral intervals, delay-distribution-dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally two numerical examples are given to illustrate the effectiveness of the proposed theoretical results. (C) 2013 Elsevier Inc. All rights reserved.-
dc.language영어-
dc.publisherELSEVIER SCIENCE INC-
dc.subjectGLOBAL ASYMPTOTIC STABILITY-
dc.subjectEXPONENTIAL STABILITY-
dc.subjectDISTRIBUTED DELAYS-
dc.subjectSYSTEMS-
dc.subjectPARAMETERS-
dc.subjectDISCRETE-
dc.subjectDESIGN-
dc.subjectCRITERIA-
dc.subjectLEAKAGE-
dc.subjectBAM-
dc.titleSampled-data state estimation for Markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays-
dc.typeArticle-
dc.identifier.wosid000324579400070-
dc.identifier.scopusid2-s2.0-84881308050-
Appears in Collections:
공과대학 > 전기공학과 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE