Encyclopedia of Systems and Control

2015 Edition
| Editors: John Baillieul, Tariq Samad

Nonlinear System Identification Using Particle Filters

  • Thomas B. Schön
Reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5058-9_106

Abstract

Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filters constitute the most popular sequential Monte Carlo (SMC) methods. 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.

Keywords

Bayesian Backward simulation Maximum likelihood Markov chain Monte Carlo (MCMC) Particle filter Particle MCMC Particle smoother Sequential Monte Carlo 
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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Thomas B. Schön
    • 1
  1. 1.Department of Information TechnologyUppsala UniversityUppsalaSweden