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Convexity Issues in System Identification
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.ORCID iD: 0000-0001-8655-2655
2013 (English)In: 10th IEEE International Conference on Control & Automation, IEEE , 2013, 1-9 p.Conference paper, Published paper (Refereed)
Abstract [en]

System Identification is about estimating models of dynamical systems from measured input-output data. Its traditional foundation is basic statistical techniques, such as maximum likelihood estimation and asymptotic analysis of bias and variance and the like. Maximum likelihood estimation relies on minimization of criterion functions that typically are non-convex, and may cause numerical search problems. Recent interest in identification algorithms has focused on techniques that are centered around convex formulations. This is partly the result of developments in machine learning and statistical learning theory. The development concerns issues of regularization for sparsity and for better tuned bias/variance trade-offs. It also involves the use of subspace methods as well as nuclear norms as proxies to rank constraints. A quite different route to convexity is to use algebraic techniques manipulate the model parameterizations. This article will illustrate all this recent development.                       

Place, publisher, year, edition, pages
IEEE , 2013. 1-9 p.
Keyword [en]
System Modelling and Identification, Learning Systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-96764DOI: 10.1109/ICCA.2013.6565206ISBN: 978-1-4673-4707-5 (print)OAI: oai:DiVA.org:liu-96764DiVA: diva2:643255
Conference
10th IEEE International Conference on Control & Automation (IEEE ICCA 2013), 12-14 June 2013, Hangzhou, China
Available from: 2013-08-26 Created: 2013-08-26 Last updated: 2016-01-11

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Ljung, LennartChen, Tianshi

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