An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search

N.H. Bergboer, V. Verdult, M.H.G. Verhaegen

    Research output: Contribution to conferencePaper

    19 Citations (Scopus)
    153 Downloads (Pure)

    Abstract

    We present a numerically efficient implementation of the nonlinear least squares and maximum likelihood identification of multivariable linear time-invariant (LTI) state-space models. This implementation is based on a local parameterization of the system and a gradient search in the resulting parameter space. The output error identification problem is discussed, and its extension to maximum likelihood identification is explained. We show that the maximum likelihood framework yields parameter errors that converge to the Cramer-Rao bound. Furthermore, the implementation is shown to be fast and able to handle large sample size problems.
    Original languageUndefined
    Pages616-621
    Publication statusPublished - 2002
    Event41st IEEE Conference on Decision and Control, CDC 2002 - Las Vegas, United States
    Duration: 10 Dec 200213 Dec 2002
    Conference number: 41

    Conference

    Conference41st IEEE Conference on Decision and Control, CDC 2002
    Abbreviated titleCDC
    CountryUnited States
    CityLas Vegas
    Period10/12/0213/12/02

    Keywords

    • Identification
    • search problems
    • least squares approximations
    • Maximum likelihood estimation
    • Linear systems
    • multivariable systems
    • State-spacemethods
    • IR-55873

    Cite this

    Bergboer, N. H., Verdult, V., & Verhaegen, M. H. G. (2002). An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search. 616-621. Paper presented at 41st IEEE Conference on Decision and Control, CDC 2002, Las Vegas, United States.
    Bergboer, N.H. ; Verdult, V. ; Verhaegen, M.H.G. / An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search. Paper presented at 41st IEEE Conference on Decision and Control, CDC 2002, Las Vegas, United States.
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    Bergboer, NH, Verdult, V & Verhaegen, MHG 2002, 'An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search' Paper presented at 41st IEEE Conference on Decision and Control, CDC 2002, Las Vegas, United States, 10/12/02 - 13/12/02, pp. 616-621.

    An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search. / Bergboer, N.H.; Verdult, V.; Verhaegen, M.H.G.

    2002. 616-621 Paper presented at 41st IEEE Conference on Decision and Control, CDC 2002, Las Vegas, United States.

    Research output: Contribution to conferencePaper

    TY - CONF

    T1 - An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search

    AU - Bergboer, N.H.

    AU - Verdult, V.

    AU - Verhaegen, M.H.G.

    PY - 2002

    Y1 - 2002

    N2 - We present a numerically efficient implementation of the nonlinear least squares and maximum likelihood identification of multivariable linear time-invariant (LTI) state-space models. This implementation is based on a local parameterization of the system and a gradient search in the resulting parameter space. The output error identification problem is discussed, and its extension to maximum likelihood identification is explained. We show that the maximum likelihood framework yields parameter errors that converge to the Cramer-Rao bound. Furthermore, the implementation is shown to be fast and able to handle large sample size problems.

    AB - We present a numerically efficient implementation of the nonlinear least squares and maximum likelihood identification of multivariable linear time-invariant (LTI) state-space models. This implementation is based on a local parameterization of the system and a gradient search in the resulting parameter space. The output error identification problem is discussed, and its extension to maximum likelihood identification is explained. We show that the maximum likelihood framework yields parameter errors that converge to the Cramer-Rao bound. Furthermore, the implementation is shown to be fast and able to handle large sample size problems.

    KW - Identification

    KW - search problems

    KW - least squares approximations

    KW - Maximum likelihood estimation

    KW - Linear systems

    KW - multivariable systems

    KW - State-spacemethods

    KW - IR-55873

    M3 - Paper

    SP - 616

    EP - 621

    ER -

    Bergboer NH, Verdult V, Verhaegen MHG. An efficient implementation of maximum likelihood identification of LTI state-space models by local gradient search. 2002. Paper presented at 41st IEEE Conference on Decision and Control, CDC 2002, Las Vegas, United States.