Study of the Effective Number of Parameters in Nonlinear Identification Benchmarks

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12 Citations (Scopus)

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 publication52nd IEEE Conference on Decision and Control, Florence, Italy, December 10-13, 2013
Pages4308-4313
Number of pages6
Publication statusPublished - 10 Dec 2013
Event52nd IEEE Conference on Decision and Control - Firenze, Italy
Duration: 10 Dec 201313 Dec 2013

Conference

Conference52nd IEEE Conference on Decision and Control
CountryItaly
CityFirenze
Period10/12/1313/12/13

Keywords

  • nonlinear identification
  • bencjhmarks

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