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Recursive Estimation Algorithms for Linear Models with Set Membership Error

  • Chapter
Bounding Approaches to System Identification

Abstract

This chapter reviews some of the more recent algorithms for sequential parameter identification in the context of unknown but bounded measurement errors when the model output is linear in the parameters. The properties of the different algorithms are analyzed and compared.

The possibility of evaluating the confidence of the obtained estimates is discussed, particularly information required on the noise structure in order to assess the confidence of the estimates is shown.

Finally, the possibility of using the algorithms for time-varying system identification is considered and the case of uncertain regressors is addressed.

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Author information

Authors and Affiliations

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

    G. Belforte

  2. Department of Electrical Engineering, National University of Singapore, 0511, Singapore

    T. T. Tay

Authors
  1. G. Belforte
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  2. T. T. Tay
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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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© 1996 Springer Science+Business Media New York

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Belforte, G., Tay, T.T. (1996). Recursive Estimation Algorithms for Linear Models with Set Membership Error. 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_6

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  • DOI: https://doi.org/10.1007/978-1-4757-9545-5_6

  • 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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