IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v30y2022i02ns0218348x22400989.html
   My bibliography  Save this article

Nonlinear Dynamic Calibration And Correction Of Acceleration Sensor Based On Adaptive Neural Network

Author

Listed:
  • SHUO XIAO

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, P. R. China)

  • SHENGZHI WANG

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, P. R. China)

  • JIAYU ZHUANG

    (��Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, P. R. China)

  • ZHENZHEN HUANG

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, P. R. China‡Library, China University of Mining and Technology, Xuzhou, P. R. China)

  • GUOPENG ZHANG

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, P. R. China)

Abstract

With the development of industrial technology, acceleration sensors have a wide range of applications. The application environment has strict requirements on acceleration sensor, which needs it to get accurate input and output response in complexity. According to the Hammerstein model, this paper studies the dynamic nonlinear relationship between the output voltage and the input acceleration of the acceleration sensor that can be divided into static nonlinear component and dynamic linear component. We combine the least square method with adaptive neural network to calculate the parameters of static nonlinear and dynamic linear components. The least square method is used to improve the training performance of the network and avoid the network falling into the local minimum of the traditional neural network. Experimental results show that compared with other methods, the proposed method has the advantages of less training steps and strong approximation ability, and the algorithm is less affected by external noise. This method can realize nonlinear system identification of acceleration sensor and provide reliable basis for compensation.

Suggested Citation

  • Shuo Xiao & Shengzhi Wang & Jiayu Zhuang & Zhenzhen Huang & Guopeng Zhang, 2022. "Nonlinear Dynamic Calibration And Correction Of Acceleration Sensor Based On Adaptive Neural Network," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(02), pages 1-10, March.
  • Handle: RePEc:wsi:fracta:v:30:y:2022:i:02:n:s0218348x22400989
    DOI: 10.1142/S0218348X22400989
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X22400989
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0218348X22400989?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.

    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:wsi:fracta:v:30:y:2022:i:02:n:s0218348x22400989. 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: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    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.