SEQUENTIAL IDENTIFICATION OF LINEAR DYNAMIC SYSTEMS IN CONTINUOUS TIME BY NOISY OBSERVATIONS.

V. A. Vasiliev, V. V. Konev

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    The problem of parameters estimation in the dynamic matrix of a linear system described by stochastic differential equations is considered. The measurements of the states of the system are supposed to be distorted by the additive white Gaussian noise. The sequential estimates which ensure the estimation of the unknown parameters and their linear combinations with the given accuracy (in mean square sense) are being constructed. A sequence of these estimates converges almost surely (a. s. ) and in mean square sense. The results may be applied to the identification of dynamic systems, in adaptive filtering and control.

    Original languageEnglish
    Pages (from-to)101-112
    Number of pages12
    JournalProblems of control and information theory
    Volume16
    Issue number2
    Publication statusPublished - 1987

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    Adaptive filtering
    Parameter estimation
    Linear systems
    Dynamical systems
    Differential equations

    ASJC Scopus subject areas

    • Engineering(all)

    Cite this

    SEQUENTIAL IDENTIFICATION OF LINEAR DYNAMIC SYSTEMS IN CONTINUOUS TIME BY NOISY OBSERVATIONS. / Vasiliev, V. A.; Konev, V. V.

    In: Problems of control and information theory, Vol. 16, No. 2, 1987, p. 101-112.

    Research output: Contribution to journalArticle

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    abstract = "The problem of parameters estimation in the dynamic matrix of a linear system described by stochastic differential equations is considered. The measurements of the states of the system are supposed to be distorted by the additive white Gaussian noise. The sequential estimates which ensure the estimation of the unknown parameters and their linear combinations with the given accuracy (in mean square sense) are being constructed. A sequence of these estimates converges almost surely (a. s. ) and in mean square sense. The results may be applied to the identification of dynamic systems, in adaptive filtering and control.",
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