Parameter identification in tidal models with uncertain boundaries

Arunabha Bagchi, Paul ten Brummelhuis

    Research output: Contribution to journalArticleAcademicpeer-review

    2 Citations (Scopus)
    114 Downloads (Pure)

    Abstract

    In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The hyperbolic nature of the governing dynamical equations is exploited in order to determine the smoothed states efficiently. This enables us to also apply the procedure to nonlinear tidal models without an excessive computational load. The main aspects of this paper are that the method of Chavent (Identification and System Parameter Estimation. Proc. 5th IFAC Symp. Pergamon, Oxford, pp 85¿97, 1979), used to calculate the gradient of a criterion that is to be minimized, is now embedded in a stochastic environment and that the estimation method can also be applied to practical, large-scale problems.
    Original languageEnglish
    Pages (from-to)745-759
    Number of pages15
    JournalAutomatica
    Volume30
    Issue number5
    DOIs
    Publication statusPublished - 1994

    Fingerprint

    Dive into the research topics of 'Parameter identification in tidal models with uncertain boundaries'. Together they form a unique fingerprint.

    Cite this