IDEAS home Printed from https://ideas.repec.org/a/hin/complx/9244890.html
   My bibliography  Save this article

Exhaustive Search and Power-Based Gradient Descent Algorithms for Time-Delayed FIR Models

Author

Listed:
  • Hua Chen
  • Yuejiang Ji
  • Daniele Salvati

Abstract

In this study, two modified gradient descent (GD) algorithms are proposed for time-delayed models. To estimate the parameters and time-delay simultaneously, a redundant rule method is introduced, which turns the time-delayed model into an augmented model. Then, two GD algorithms can be used to identify the time-delayed model. Compared with the traditional GD algorithms, these two modified GD algorithms have the following advantages: (1) avoid a high-order matrix eigenvalue calculation, thus, are more efficient for large-scale systems; (2) have faster convergence rates, therefore, are more practical in engineering practices. The convergence properties and simulation examples are presented to illustrate the efficiency of the two algorithms.

Suggested Citation

  • Hua Chen & Yuejiang Ji & Daniele Salvati, 2022. "Exhaustive Search and Power-Based Gradient Descent Algorithms for Time-Delayed FIR Models," Complexity, Hindawi, vol. 2022, pages 1-10, September.
    • Handle: RePEc:hin:complx:9244890
    DOI: 10.1155/2022/9244890
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2022/9244890.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/complexity/2022/9244890.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9244890?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:hin:complx:9244890. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.