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Worst-Case l 1 Identification

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Bounding Approaches to System Identification

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Abstract

In this chapter recent results on nonparametric and mixed parametric-nonparametric l 1 identification are reviewed. These results mainly concern the evaluation of the identification errors, the design of experiment, the selection of the model structure, the construction of optimal and almost optimal algorithms, and the convergence properties of the identification algorithms.

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Author information

Authors and Affiliations

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

    M. Milanese

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  1. M. Milanese
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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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Milanese, M. (1996). Worst-Case l 1 Identification. 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_11

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  • DOI: https://doi.org/10.1007/978-1-4757-9545-5_11

  • 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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