IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9173870.html
   My bibliography  Save this article

Research on Road Roughness Based on NARX Neural Network

Author

Listed:
  • Yingjie Liu
  • Dawei Cui

Abstract

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

Suggested Citation

  • Yingjie Liu & Dawei Cui, 2021. "Research on Road Roughness Based on NARX Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, December.
    • Handle: RePEc:hin:jnlmpe:9173870
    DOI: 10.1155/2021/9173870
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/9173870.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/9173870.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/9173870?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:9173870. 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.