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Noréus, Olof
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Publications (2 of 2) Show all publications
Wallin, R., Isaksson, A. J. & Noréus, O. (2001). Extensions to "output prediction under scarce data operation: Control applications". Automatica, 37(12), 2069-2071
Open this publication in new window or tab >>Extensions to "output prediction under scarce data operation: Control applications"
2001 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 37, no 12, p. 2069-2071Article in journal (Refereed) Published
Abstract [en]

Albertos et al. (Automatica, 35 (1999) 1671-1681), proposed a simple and computationally cheap output estimation algorithm for systems where some output data is missing. In the original paper, a stability analysis-of the algorithm is provided for the special case that every Nth sample of the output is observed. We here show how the stability can be analysed for arbitrary periodical missing data patterns.

Keywords
Missing data, Output estimation, State estimation, Unconventional sampling
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-13567 (URN)10.1016/S0005-1098(01)00161-3 (DOI)000171914000021 ()2-s2.0-0035546383 (Scopus ID)
Available from: 2011-04-29 Created: 2011-04-19 Last updated: 2017-12-11Bibliographically approved
Noreus, O., Isaksson, A. J. & Gulliksson, M. (2000). Estimation of Missing Data for Model Validation. In: Control Systems, Preprints, Conference: . Paper presented at Control Systems 2000 'Quantifying the Benefits of Process Control'; Victoria, BC, Can; ; 1 May 2000 through 4 May 2000 (pp. 89-95). TAPPI Press
Open this publication in new window or tab >>Estimation of Missing Data for Model Validation
2000 (English)In: Control Systems, Preprints, Conference, TAPPI Press, 2000, p. 89-95Conference paper, Published paper (Other academic)
Abstract [en]

To be able to validate models based on signals logged from processes, it is necessary to have a continuous data set. Since missing data is a common problem when logging process variables, it can be almost impossible to find a continuous data set. In general it might be necessary to replace missing data also when estimating models for the signals and this paper compares different approaches for missing data estimation. The result shows that on the available data set there is no significant difference in the performance and for the model validation problem it is probably enough to use the simplest method. However, for other applications and on other data sets, there are probably important differences.

Place, publisher, year, edition, pages
TAPPI Press, 2000
Keywords
Missing Data, Model Validation
National Category
Mathematics
Identifiers
urn:nbn:se:miun:diva-3788 (URN)2-s2.0-0033717548 (Scopus ID)4408 (Local ID)1896742599 (ISBN)4408 (Archive number)4408 (OAI)
Conference
Control Systems 2000 'Quantifying the Benefits of Process Control'; Victoria, BC, Can; ; 1 May 2000 through 4 May 2000
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2016-10-10Bibliographically approved
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