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Accuracy analysis of time domain maximum likelihood method and sample maximum likelihood method for errors-in-variables and output error identification
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
VUB, Brussels.
VUB, Brussels.
2010 (English)In: Automatica, ISSN 0005-1098, Vol. 46, no 4, 721-727 p.Article in journal (Refereed) Published
Abstract [en]

For identifying errors-in-variables models, the time domain maximum likelihood (TML) method and the sample maximum likelihood (SML) method are two approaches. Both methods give optimal estimation accuracy but under different assumptions. In the TML method, an important assumption is that the noise-free input signal is modelled as a stationary process with rational spectrum. For SML, the noise-free input needs to be periodic. It is interesting to know which of these assumptions contain more information to boost the estimation performance. In this paper, the estimation accuracy of the two methods is analyzed statistically for both errors-in-variables (EIV) and output error models (OEM). Numerical comparisons between these two estimates are also done under different signal-to-noise ratios (SNRs). The results suggest that TML and SML have similar estimation accuracy at moderate or high SNR for Ely. For OEM identification, these two methods have the same accuracy at any SNR.

Place, publisher, year, edition, pages
2010. Vol. 46, no 4, 721-727 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-136110DOI: 10.1016/j.automatica.2010.01.026ISI: 000276755900012OAI: oai:DiVA.org:uu-136110DiVA: diva2:376251
Available from: 2010-12-10 Created: 2010-12-10 Last updated: 2011-01-05Bibliographically approved

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