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The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
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.
1994 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 23, no 5, 329-338 p.Article in journal (Refereed) Published
Abstract [en]

The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.

Place, publisher, year, edition, pages
1994. Vol. 23, no 5, 329-338 p.
Keyword [en]
Worst-case identification, FIR systems, Least-squares algorithm
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56339DOI: 10.1016/0167-6911(94)90065-5OAI: oai:DiVA.org:liu-56339DiVA: diva2:318615
Available from: 2010-05-09 Created: 2010-05-07 Last updated: 2017-12-12

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