Instrumental variable methods for identifying partial differential equation models

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dc.contributor.authorSchorsch, Julien
dc.contributor.authorGarnier, H.
dc.contributor.authorGilson, M.
dc.contributor.authorYoung, Peter C
dc.date.accessioned2015-12-13T22:39:43Z
dc.date.issued2013
dc.date.updated2015-12-11T09:51:51Z
dc.description.abstractThis paper presents a refined instrumental variable method for identifying partial differential equation models of distributed parameter systems directly from discrete-time sampled input-output data. The proposed method is compared with conventional least
dc.identifier.issn0020-7179
dc.identifier.urihttp://hdl.handle.net/1885/77899
dc.publisherTaylor & Francis Group
dc.sourceInternational Journal of Control
dc.titleInstrumental variable methods for identifying partial differential equation models
dc.typeJournal article
local.bibliographicCitation.issue12
local.bibliographicCitation.lastpage2335
local.bibliographicCitation.startpage2325
local.contributor.affiliationSchorsch, Julien, Universite de Lorraine
local.contributor.affiliationGarnier, H., Universite de Lorraine
local.contributor.affiliationGilson, M., Nancy-Universite
local.contributor.affiliationYoung, Peter C, College of Medicine, Biology and Environment, ANU
local.contributor.authoremailrepository.admin@anu.edu.au
local.contributor.authoruidYoung, Peter C, u5092275
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor109999 - Technology not elsewhere classified
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
local.identifier.ariespublicationf5625xPUB6640
local.identifier.citationvolume86
local.identifier.doi10.1080/00207179.2013.813690
local.identifier.scopusID2-s2.0-84888874226
local.identifier.uidSubmittedByf5625
local.type.statusPublished Version

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