Title :
A balanced model reduction method for a class of matrix inversion problems with parametric uncertainty
Author_Institution :
Dept. of Math., Kaiserslautern Univ., Germany
fDate :
6/21/1905 12:00:00 AM
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;
Conference_Titel :
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Print_ISBN :
0-7803-5250-5
DOI :
10.1109/CDC.1999.832915
