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A mathematica toolbox for signals, models and identification
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2012 (English)In: 16th IFAC Symposium on System Identification, IFAC , 2012, no PART 1, 1541-1546 p.Conference paper (Refereed)
Abstract [en]

In this contribution we provide a status report for the Mathematica toolbox that is described in?. The toolbox covers a comprehensive set of functions for handling deterministic and stochastic signals and models. On top of this the toolbox provides signal processing and system identification methods ranging from non-parametric to parametric, and from linear models to a wide class of non-linear models. Algorithms are tailored to be able to efficiently handle large scale data sets and models as well as symbolic computations. This allows theory to be handled alongside practice, implying that the toolbox provides an environment suitable both for education and data processing. In regards to system identification, one of the novel features is graphical support for building block-based nonlinear models. Another novel feature is that modeling errors can be propagated through applications.

Place, publisher, year, edition, pages
IFAC , 2012. no PART 1, 1541-1546 p.
, IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; 16
Keyword [en]
Deterministic modeling, Linear systems nonlinear systems, Signal processing, Stochastic modeling, System identification
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-105441DOI: 10.3182/20120711-3-BE-2027.00273ScopusID: 2-s2.0-84867051127ISBN: 978-390282306-9OAI: diva2:571456
Universite Libre de Bruxelles, 11 July 2012 through 13 July 2012, Bruxelles
ICT - The Next Generation

QC 20121122

Available from: 2012-11-22 Created: 2012-11-21 Last updated: 2013-04-11Bibliographically approved

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Hjalmarsson, Håkan
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Automatic ControlACCESS Linnaeus Centre
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