Study of the effective number of parameters in nonlinear identification benchmarks

Anna Marconato, Maarten Schoukens, Yves Rolain, J.F.M. Schoukens

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

12 Citations (Scopus)
1 Downloads (Pure)

Abstract

This paper discusses the importance of the notion of effective number of parameters as a measure of model complexity. Exploiting this concept allows a fair comparison of models obtained from different model classes. Several illustrative examples of linear and nonlinear models are presented to provide more insight in the problem. As one possible way of showing that model complexity can be reduced without having to pull any parameters to zero, an approach for rank reduced estimation based on the truncated SVD is also discussed. These ideas are then applied to two nonlinear real world problems: The Wiener-Hammerstein and the Silverbox benchmarks.

Original languageEnglish
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers
Pages4308-4313
Number of pages6
ISBN (Print)9781467357173
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event52nd IEEE Conference on Decision and Control (CDC 2013) - Florence, Italy
Duration: 10 Dec 201313 Dec 2013
Conference number: 52

Conference

Conference52nd IEEE Conference on Decision and Control (CDC 2013)
Abbreviated titleCDC 2013
CountryItaly
CityFlorence
Period10/12/1313/12/13

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