IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5555054.html
   My bibliography  Save this article

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Author

Listed:
  • Minh-Canh Huynh
  • Cheng-Yuan Chang

Abstract

Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.

Suggested Citation

  • Minh-Canh Huynh & Cheng-Yuan Chang, 2021. "Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, May.
    • Handle: RePEc:hin:jnlmpe:5555054
    DOI: 10.1155/2021/5555054
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/5555054.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/5555054.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/5555054?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:jnlmpe:5555054. 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.