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New Convergence Results for Least Squares Identification Algorithm
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.
2008 (English)In: Proceedings of the 17th IFAC World Congress, 2008, 5030-5035 p.Conference paper, Published paper (Refereed)
Abstract [en]

The basic least squares method for identifying linear systems has been extensively studied. Conditions for convergence involve issues about noise assumptions and behavior of the sample covariance matrix of the regressors. Lai and Wei proved in 1982 convergence for essentially minimal conditions on the regression matrix: All eigenvalues must tend to infinity, and the logarithm of the largest eigenvalue must not tend to infinity faster than the smallest eigenvalue. In this contribution we revisit this classical result with respect to assumptions on the noise: How much unstructured disturbances can be allowed without affecting the convergence? The answer is that the norm of these disturbances must tend to infinity slower than the smallest eigenvalue of the regression matrix.

Place, publisher, year, edition, pages
2008. 5030-5035 p.
Keyword [en]
System identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-44706DOI: 10.3182/20080706-5-KR-1001.00845Local ID: 77386ISBN: 978-3-902661-00-5 (print)OAI: oai:DiVA.org:liu-44706DiVA: diva2:265568
Conference
17th IFAC World Congress, Seoul, South Korea, July, 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-09-15

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Hu, Xiao-LiLjung, Lennart

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