dc.contributor.author Chavent, Guy dc.date.accessioned 2014-08-28T13:12:02Z dc.date.available 2014-08-28T13:12:02Z dc.date.issued 1987 dc.identifier.uri https://basepub.dauphine.fr/handle/123456789/13876 dc.language.iso en en dc.subject Identifiability en dc.subject parameter estimation en dc.subject regularization en dc.subject.ddc 519 en dc.title Identifiability of Parameters in the Output Least Square Formulation en dc.type Chapitre d'ouvrage dc.description.abstracten Given a mathematical model of a physical process, the parameter identification problem is defined as that of determining the parameters of the model that, for a known given input, yield a given measured output. Identifiability is often defined as the injectivity of the parameter → output mapping defined by the mathematical model used. Though already this definition is difficult to check in practical situations, it is not guaranteed, when it is satisfied, that parameters can be determined in a unique and stable way from the measurement. So we will review some recent definitions as OLS-Stability, OLS-identifiability and δ-Identifiability. Two different ways of obtaining OLSI will be given: either by studying the injectivity of the linearized parameter → output mapping (the “sensitivity matrix”), which will yield OLSI for finite dimensional parameters, but will usually be of little help for infinite dimensional parameters, or by using a regularization technique, which will be shown to yield OLSI for a large enough regularization parameter. en dc.identifier.citationpages 67-74 en dc.relation.ispartoftitle Identifiability of Parametric Models en dc.relation.ispartofeditor Walter, Eric dc.relation.ispartofpublname Elsevier en dc.relation.ispartofdate 1987 dc.relation.ispartofpages 119 en dc.subject.ddclabel Probabilités et mathématiques appliquées en dc.relation.ispartofisbn 978-0-08-034929-9 en dc.relation.forthcoming non en dc.identifier.doi http://dx.doi.org/10.1016/B978-0-08-034929-9.50010-8 en
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