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Segmentation of Time Series from Nonlinear Dynamical Systems
Katholieke Universiteit Leuven, Belgium.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Katholieke Universiteit Leuven, Belgium.
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2011 (English)In: Proceedings of the 18th IFAC World Congress, 2011, 13209-13214 p.Conference paper, Published paper (Refereed)
Abstract [en]

Segmentation of time series data is of interest in many applications, as for example in change detection and fault detection. In the area of convex optimization, the sum-of-norms regularization has recently proven useful for segmentation. Proposed formulations handle linear models, like ARX models, but cannot handle nonlinear models. To handle nonlinear dynamics, we propose integrating the sum-of-norms regularization with a least squares support vector machine (LS-SVM) core model. The proposed formulation takes the form of a convex optimization problem with the regularization constant trading off the fit and the number of segments.

Place, publisher, year, edition, pages
2011. 13209-13214 p.
Keyword [en]
Convex optimization, System identification, Support vector machines, Failure detection, Nonlinear systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-95594DOI: 10.3182/20110828-6-IT-1002.01988ISBN: 978-3-902661-93-7 (print)OAI: oai:DiVA.org:liu-95594DiVA: diva2:636436
Conference
18th IFAC World Congress, Milano, Italy, 28 August-2 September, 2011
Available from: 2013-07-10 Created: 2013-07-10 Last updated: 2013-07-10

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Ohlsson, HenrikLjung, Lennart

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CiteExportLink to record
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Citation style
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  • sv-SE
  • Other locale
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Output format
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