IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v44y2013i7p1200-1208.html
   My bibliography  Save this article

Joint state and parameter estimation for uncertain stochastic nonlinear polynomial systems

Author

Listed:
  • Michael Basin
  • Alexander Loukianov
  • Miguel Hernandez-Gonzalez

Abstract

This article presents the joint state filtering and parameter identification problem for uncertain stochastic nonlinear polynomial systems with unknown parameters in the state equation over nonlinear polynomial observations, where the unknown parameters are considered Wiener processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. The obtained mean-square filter for the extended state vector also serves as the mean-square identifier for the unknown parameters. Performance of the designed mean-square state filter and parameter identifier is verified for both, positive and negative, parameter values.

Suggested Citation

  • Michael Basin & Alexander Loukianov & Miguel Hernandez-Gonzalez, 2013. "Joint state and parameter estimation for uncertain stochastic nonlinear polynomial systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1200-1208.
  • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:7:p:1200-1208
    DOI: 10.1080/00207721.2012.670309
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2012.670309
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207721.2012.670309?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinjiang Wang & Robert X. Gao & Zhuang Yuan & Zhaoyan Fan & Laibin Zhang, 2019. "A joint particle filter and expectation maximization approach to machine condition prognosis," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 605-621, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:44:y:2013:i:7:p:1200-1208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.