• DocumentCode
    2571829
  • Title

    Estimation of general nonlinear state-space systems

  • Author

    Ninness, Brett ; Wills, Adrian ; Schön, Thomas B.

  • Author_Institution
    Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW, Australia
  • fYear
    2010
  • fDate
    15-17 Dec. 2010
  • Firstpage
    6371
  • Lastpage
    6376
  • Abstract
    This paper presents a novel approach to the estimation of a general class of dynamic nonlinear system models. The main contribution is the use of a tool from mathematical statistics, known as Fishers´ identity, to establish how so-called “particle smoothing” methods may be employed to compute gradients of maximum-likelihood and associated prediction error cost criteria.
  • Keywords
    maximum likelihood estimation; state-space methods; statistics; Fishers identity; dynamic nonlinear system model; general nonlinear state-space system; mathematical statistics; maximum likelihood; particle smoothing; prediction error cost criteria; Approximation methods; Computational modeling; Markov processes; Mathematical model; Maximum likelihood estimation; Yttrium;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2010 49th IEEE Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    0743-1546
  • Print_ISBN
    978-1-4244-7745-6
  • Type

    conf

  • DOI
    10.1109/CDC.2010.5717378
  • Filename
    5717378
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2571829