Parameter sensitivity, estimation and convergence: an information approach

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dc.contributor.authorDeBrunner, Victor Earlen
dc.contributor.committeechairBeex, A. A. Louisen
dc.contributor.committeememberBeattie, Christopher A.en
dc.contributor.committeememberBingular, Stanoje P.en
dc.contributor.committeememberLindner, Douglas K.en
dc.contributor.committeememberRiad, Sedki Mohameden
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T21:21:17Zen
dc.date.adate2005-10-14en
dc.date.available2014-03-14T21:21:17Zen
dc.date.issued1990-04-18en
dc.date.rdate2005-10-14en
dc.date.sdate2005-10-14en
dc.description.abstractConvergence rates are analyzed for Recursive Prediction (Output) Error Methods (RPEM) in the identification of linear state-space systems from (noisy) impulse response data) RPEM algorithms are derived which are suitable for the identification of the parameters in arbitrary state-space structures. Deterministic and stochastic versions of these identification algorithms are presented. These two classes indicate the number of realizations used in the identification, not the presence or absence of noise. The convergence analysis uses the eigen-information of the correlation matrix (really its inverse, the Fisher information matrix) for a chosen parameterization. This analysis explains why various state-space structures have different convergence properties, 1.e., why for the same system the estimation processes corresponding to different identification structures converge at different rates. The eigen-information of the parameter information matrix relates the system sensitivity and numerical conditioning in a manner which provides insight into the identification process. The relevant eigen-information is combined in the proposed scalar convergence time constant +. One important result is that identification of the usually identified direct form II parameters (the standard ARMA parameters) does not necessarily yield the fastest parameter set convergence for the system being identified. Identification from arbitrary input is also briefly considered, as is identification when the model order is different from the “true” system order.en
dc.description.degreePh. D.en
dc.format.extentxi, 180 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-10142005-135736en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10142005-135736/en
dc.identifier.urihttp://hdl.handle.net/10919/39893en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1990.D437.pdfen
dc.relation.isformatofOCLC# 22250619en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1990.D437en
dc.subject.lcshConvergence -- Researchen
dc.titleParameter sensitivity, estimation and convergence: an information approachen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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