A balanced model reduction method for a class of matrix inversion problems with parametric uncertainty

Author

Zerz, Eva

Author_Institution

Dept. of Math., Kaiserslautern Univ., Germany

Volume

1

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

948

Abstract

A model reduction method is proposed for parameter-dependent matrix inversion problems, in which the matrix entries are rational functions of the parameters. Its goal is to reduce the complexity of symbolic expressions that appear in the inverse, taking into account parametric uncertainty. The complexity reduction and its error bound are based on existing balanced model reduction techniques for linear fractional transformations. The method is applied to system matrices that arise from the modeling of electrical networks

Keywords

computational complexity; errors; matrix inversion; network analysis; rational functions; reduced order systems; uncertain systems; balanced model reduction method; electrical network modeling; error bound; linear fractional transformations; parameter-dependent matrix inversion problems; parametric uncertainty; rational functions; symbolic expression complexity reduction; system matrices; Algebra; Application software; Computer applications; Context modeling; Electronic mail; Mathematics; Reduced order systems; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-5250-5

Type

conf

DOI

10.1109/CDC.1999.832915

Filename

832915