Model order reduction of fully parameterized systems by recursive least square optimization

Zheng Zhang, Ibrahim M. Elfadel, Luca Daniel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper presents an approach for the model order reduction of fully parameterized linear dynamic systems. In a fully parameterized system, not only the state matrices, but also can the input/output matrices be parameterized. The algorithm presented in this paper is based on neither conventional moment-matching nor balanced-truncation ideas. Instead, it uses optimal (block) vectors to construct the projection matrix, such that the system errors in the whole parameter space are minimized. This minimization problem is formulated as a recursive least square (RLS) optimization and then solved at a low cost. Our algorithm is tested by a set of multi-port multi-parameter cases with both intermediate and large parameter variations. The numerical results show that high accuracy is guaranteed, and that very compact models can be obtained for multi-parameter models due to the fact that the ROM size is independent of the number of parameters in our approach.

Original languageBritish English
Title of host publication2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011
Pages523-530
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011 - San Jose, CA, United States
Duration: 7 Nov 201110 Nov 2011

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Conference

Conference2011 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2011
Country/TerritoryUnited States
CitySan Jose, CA
Period7/11/1110/11/11

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