Towards a behavioral approach to linear approximate modeling

In 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Engineering

Grantor

Rice University

Year dc:date.issued

1993

Author dc:creator

• Gatt, George John

Advisor dc:contributor.advisor

• Antoulas, Athanasios C.

• Electronics
• Electrical engineering
• System science
• Applied mechanics

Statement dc:rights

• Copyright 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.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/13728

OAI identifier oai:identifier

oai:repository.rice.edu:1911/13728