IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v55y2024i8p1737-1753.html
   My bibliography  Save this article

Identification of nonlinear system with time delay based on wavelet packet decomposition and Gaussian kernel GMDH network

Author

Listed:
  • Jun Yan
  • Junhong Li
  • Guixiang Bai
  • Tiancheng Zong

Abstract

This article addresses the identification problem of the input nonlinear closed-loop system with unknown time delay (INCTD). An identification algorithm of the INCTD system which combines the wavelet packet decomposition (WPD), the variance Gaussian kernel function (VGKF) and the grouped method of data handling (GMDH) network is proposed. The data collected by the sensor is decomposed into intrinsic mode function with different nonlinear characteristics using WPD. The GMDH network is applied to mine bidirectional strong nonlinear relationship between input and output data. The VGKF criterion selects the optimal complexity model to enhance the extrapolation performance, and further improves the identification accuracy. Thus, the WPD-GMDH-VGKF identification algorithm is derived. The numerical simulation result verifies that the WPD-GMDH-VGKF identification method can effectively identify the INCTD system, and it is superior to GMDH and WPD-GMDH in terms of identification accuracy. The application case verifies the feasibility of applying the WPD-GMDH-VGKF identification method to the battery management system (BMS).

Suggested Citation

  • Jun Yan & Junhong Li & Guixiang Bai & Tiancheng Zong, 2024. "Identification of nonlinear system with time delay based on wavelet packet decomposition and Gaussian kernel GMDH network," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(8), pages 1737-1753, June.
  • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:8:p:1737-1753
    DOI: 10.1080/00207721.2024.2317354
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2024.2317354
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207721.2024.2317354?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.

    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:55:y:2024:i:8:p:1737-1753. 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.