Gradient-based approaches for recursive Frisch scheme identification

J. G. Linden, B. Vinsonneau, Keith J. Burnham

    Research output: Chapter in Book/Report/Conference proceedingChapter


    An algorithm for recursive Frisch scheme system identification of linear single-input single-output errors-in-variables systems is developed. For the update of the estimated model parameters, a recursive bias-compensating least squares algorithm, which is based on the well-known recursive least squares technique, is considered. The estimate of the output measurement noise variance is determined using a conjugate gradient method, which tracks the smallest eigenvalue of a slowly varying matrix. For the update of the input measurement noise estimate, a steepest gradient search is applied. It tracks the minimum of a model selection cost function, which is based on a set of high order Yule-Walker equations.
    Original languageEnglish
    Title of host publicationIFAC Proceedings Volumes
    Publication statusPublished - 2008

    Bibliographical note

    The full text is not available on the repository.


    • Errors in variables identification
    • Recursive identification
    • Estimation and filtering


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