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  • 1.
    Noreus, Olof
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Isaksson, Alf J.
    Gulliksson, Mårten
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Estimation of Missing Data for Model Validation2000In: Control Systems, Preprints, Conference, TAPPI Press, 2000, p. 89-95Conference 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.

  • 2.
    Wallin, R.
    et al.
    Department of Signals, Sensors and Systems, Process Control, Royal Institute of Technology, SE100 44 Stockholm.
    Isaksson, A. J.
    Department of Signals, Sensors and Systems, Process Control, Royal Institute of Technology, SE100 44 Stockholm.
    Noréus, Olof
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    Extensions to "output prediction under scarce data operation: Control applications"2001In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 37, no 12, p. 2069-2071Article in journal (Refereed)
    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.

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