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Subspace System Identification via Weighted Nuclear Norm Optimization in 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), vol , issue , pp 3439-3444
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Northrop Grumman Corporation, San Diego, USA.
UCLA, Los Angels, USA.
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a subspace system identification method based on weighted nuclear norm approximation. The weight matrices used in the nuclear norm minimization are the same weights as used in standard subspace identification methods. We show that the inclusion of the weights improves the performance in terms of fit on validation data. Experimental results from randomly generated examples as well as from the Daisy benchmark collection are reported. The key to an efficient implementation is the use of the alternating direction method of multipliers to solve the optimization problem.

Place, publisher, year, edition, pages
IEEE; 1998 , 2012. 3439-3444 p.
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Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-103269DOI: 10.1109/CDC.2012.6426980ISI: 000327200403126ISBN: 978-1-4673-2064-1 (print)ISBN: 978-1-4673-2065-8 (print)OAI: oai:DiVA.org:liu-103269DiVA: diva2:688522
Conference
IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
Available from: 2014-01-17 Created: 2014-01-16 Last updated: 2014-11-05

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Hansson, Anders

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