Explicit linear regressive model structures for estimation, prediction and experimental design in compartmental diffusive systems

Dirk Vries, Karel Keesman, Hans Zwart

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    A linear regressive model structure and output predictor, both in algebraic form, are deduced from an LTI state space system with certain properties without the need of direct matrix inversion. On the basis of this, explicit expressions of parametric sensitivities are given. As an example, a diffusion process is approximated by a state space discrete time model with n compartments in the spatial plane and is then reparametrized. The system output can then be explicitly predicted by ŷk = θT φk-n - ेk-n as a function of n, the sensor position, the parameter vector θ, and input-output data. This method is attractive for estimation, prediction and insight in experimental design issues, when physical knowledge is to be preserved.
    Original languageEnglish
    Title of host publication14th IFAC Symposium on Identification and System Parameter Estimation
    PublisherIFAC
    Pages404-408
    Number of pages5
    DOIs
    Publication statusPublished - 2006
    Event14th IFAC Symposium on Systems Identification, SYSID 2006 - Newcastle, Australia
    Duration: 29 Mar 200631 Mar 2006
    Conference number: 14

    Publication series

    NameIFAC Proceedings
    PublisherIFAC
    Number1
    Volume39

    Conference

    Conference14th IFAC Symposium on Systems Identification, SYSID 2006
    Abbreviated titleSYSID
    CountryAustralia
    CityNewcastle
    Period29/03/0631/03/06

    Keywords

    • IR-62370
    • EWI-12968
    • MSC-93B50

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