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Nonlinear System Identification Using Particle Filters

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Encyclopedia of Systems and Control

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Abstract

Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filter constitute the most popular sequential Monte Carlo (SMC) method. This is a relatively recent development, and the aim here is to provide a brief exposition of these SMC methods and how they are key enabling algorithms in solving nonlinear system identification problems. The particle filters are important for both frequentist (maximum likelihood) and Bayesian nonlinear system identification.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Thomas B. Schön

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  1. Thomas B. Schön
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Correspondence to Thomas B. Schön .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, MA, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, MN, USA

    Tariq Samad

Section Editor information

  1. Division of Automatic Control, Department of Electrical Engineering, Linkö** University, Linkö**, Sweden

    Lennart Ljung

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Schön, T.B. (2020). Nonlinear System Identification Using Particle Filters. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_106-2

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_106-2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5102-9

  • Online ISBN: 978-1-4471-5102-9

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Chapter history

  1. Latest

    Nonlinear System Identification Using Particle Filters
    Published:
    12 December 2019

    DOI: https://doi.org/10.1007/978-1-4471-5102-9_106-2

  2. Original

    Nonlinear System Identification Using Particle Filters
    Published:
    26 March 2014

    DOI: https://doi.org/10.1007/978-1-4471-5102-9_106-1

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