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Transfer Function Parameter Interval Estimation Using Recursive Least Squares in the Time and Frequency Domains

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Bounding Approaches to System Identification

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Abstract

A bank of recursive least squares (RLS) estimators is proposed for the estimation of the uncertainty intervals of the parameters of an equation error model (or RLS model), where the equation error is assumed to lie between a known upper and lower bound. It is shown that the off-line least squares method gives the maximum and minimum parameter values that could have produced the recorded input-output sequence. By modifying the RLS estimator in two ways, it is possible to recursively compute inner and outer bounds of the uncertainty intervals. It is shown that the inner bound is asymptotically tight. It is demonstrated that transfer function parameter intervals can also be estimated, by applying the method to measured frequency function data.

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Author information

Authors and Affiliations

  1. Faculty of Agricultural Engineering, Technion—Israel Institute of Technology, Haifa, 32000, Israel

    P.-O. Gutman

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  1. P.-O. Gutman
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Editor information

Editors and Affiliations

  1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, 10129, Italy

    Mario Milanese

  2. School of Electronic and Electrical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    John Norton

  3. Direction des Études de Synthèse, SM Office National d’Études et de Recherches Aérospatiales, Châtillon Cedex, F-92322, France

    Hélène Piet-Lahanier

  4. Laboratoire des Signaux et Systèmes, CNRS-École Supérieure d’Électricité, Gif-sur-Yvette Cedex, 91192, France

    Éric Walter

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Gutman, PO. (1996). Transfer Function Parameter Interval Estimation Using Recursive Least Squares in the Time and Frequency Domains. In: Milanese, M., Norton, J., Piet-Lahanier, H., Walter, É. (eds) Bounding Approaches to System Identification. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9545-5_7

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  • DOI: https://doi.org/10.1007/978-1-4757-9545-5_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-9547-9

  • Online ISBN: 978-1-4757-9545-5

  • eBook Packages: Springer Book Archive

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