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A Critical View on Benchmarks based on Randomly Generated Systems
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-8524-0649
2015 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 48, no 28, 1471-1476 p.Article in journal (Refereed) Published
Abstract [en]

In data-based modelling communities, such as system identification, machine learning, signal processing and statistics, benchmarks are essential for testing and comparing old and new techniques for the estimation of models. During the last years, it has become customary in system identification to rely on data sets built from randomly generated systems. In this article we discuss the implications of this practice, in particular when using data sets generated with the MATLABr command drss, and advocate the cautious use of comparisons based on these benchmarks.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 48, no 28, 1471-1476 p.
Keyword [en]
Artificial intelligence, Learning systems, Religious buildings, Data-based modelling, Signal processing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-195491DOI: 10.1016/j.ifacol.2015.12.341ScopusID: 2-s2.0-84988503972OAI: oai:DiVA.org:kth-195491DiVA: diva2:1049782
Note

QC 20161125

Available from: 2016-11-25 Created: 2016-11-03 Last updated: 2016-11-25Bibliographically approved

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Rojas, Cristian R.Valenzuela, Patricio Esteban
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