IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v34y2018i5p659-666.html
   My bibliography  Save this article

Nonlinearity recovery by standard and aggregative orthogonal series algorithms

Author

Listed:
  • Przemysław Śliwiński
  • Paweł Wachel
  • Szymon Łagosz

Abstract

In this paper, the problem of nonlinearity recovery in Hammerstein systems is considered. Two algorithms are presented: the first is a standard orthogonal series algorithm, whereas the other, ie, the aggregative one, exploits the convex programming approach. The finite sample size properties of both approaches are examined, compared, and illustrated in a numerical experiment. The aggregative algorithm performs better when the number of measurements is comparable to the number of parameters; however, it also imposes additional smoothness restrictions on the recovered nonlinearities.

Suggested Citation

  • Przemysław Śliwiński & Paweł Wachel & Szymon Łagosz, 2018. "Nonlinearity recovery by standard and aggregative orthogonal series algorithms," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 34(5), pages 659-666, September.
    • Handle: RePEc:wly:apsmbi:v:34:y:2018:i:5:p:659-666
    DOI: 10.1002/asmb.2311
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2311
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2311?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
    ---><---

    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:wly:apsmbi:v:34:y:2018:i:5:p:659-666. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.