hig.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fractional models for modeling complex linear systems under poor frequency resolution measurements
Vrije Universiteit Brussel, Brussels, Belgium.
Vrije Universiteit Brussel, Brussels, Belgium.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences. Vrije Universiteit Brussel, Brussels, Belgium.
Vrije Universiteit Brussel, Brussels, Belgium.
2013 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 23, no 4, 1084-1093 p.Article in journal (Refereed) Published
Abstract [en]

When modeling a linear system in a parametric way, one needs to deal with (i) model structure selection, (ii) model order selection as well as (iii) an accurate fit of the model. The most popular model structure for linear systems has a rational form which reveals crucial physical information and insight due to the accessibility of poles and zeros. In the model order selection step, one needs to specify the number of poles and zeros in the model. Automated model order selectors like Akaikeʼs Information Criterion (AIC) and the Minimum Description Length (MDL) are popular choices. A large model order in combination with poles and zeros lying closer to each other in frequency than the frequency resolution indicates that the modeled system exhibits some fractional behavior. Classical integer order techniques cannot handle this fractional behavior due to the fact that the poles and zeros are lying to close to each other to be resolvable and not enough data is available for the classical integer order identification procedure. In this paper, we study the use of fractional order poles and zeros and introduce a fully automated algorithm which (i) estimates a large integer order model, (ii) detects the fractional behavior, and (iii) identifies a fractional order system.

Place, publisher, year, edition, pages
2013. Vol. 23, no 4, 1084-1093 p.
Keyword [en]
Transfer function, Nonlinear least squares, Linear systems, Parametric models, Fractional order systems, Non-asymptotic, Statistical signal processing, Continuous-time modeling, Poor frequency resolutions
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hig:diva-16002DOI: 10.1016/j.dsp.2013.01.009ISI: 000319180200002Scopus ID: 2-s2.0-84877585273OAI: oai:DiVA.org:hig-16002DiVA: diva2:687308
Available from: 2014-01-14 Created: 2014-01-14 Last updated: 2016-06-29Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Van Moer, Wendy
By organisation
Department of Electronics, Mathematics and Natural Sciences
In the same journal
Digital signal processing (Print)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 59 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf