Towards a behavioral approach to linear approximate modeling

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dc.contributor.advisorAntoulas, Athanasios C.
dc.creatorGatt, George John
dc.date.accessioned2009-06-04T00:40:42Z
dc.date.available2009-06-04T00:40:42Z
dc.date.issued1993
dc.description.abstractIn this thesis, the foundations for the development of a behavioral approach to linear approximate modeling, are established. A particular data set, consisting of stable, discrete-time, purely exponential time series and a specific class of dynamical models are considered. A misfit function, between the data measurements and a system, belonging to this model class, is defined and the problem of characterizing all members of our model class, for which the value of the misfit function remains below a prespecified error level, is addressed. The concept of the block Hankel matrix, constructed from the data measurements, is then introduced, and it is shown that the optimal Hankel-norm approximation theory provides the main tool for a partial solution of the above problem.
dc.format.extent114 p.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.callnoThesis E.E. 1993 Gatt
dc.identifier.citationGatt, George John. "Towards a behavioral approach to linear approximate modeling." (1993) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/13728">https://hdl.handle.net/1911/13728</a>.
dc.identifier.urihttps://hdl.handle.net/1911/13728
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectElectronics
dc.subjectElectrical engineering
dc.subjectSystem science
dc.subjectApplied mechanics
dc.titleTowards a behavioral approach to linear approximate modeling
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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