IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i11p1801-d1666530.html
   My bibliography  Save this article

Advances in Zeroing Neural Networks: Convergence Optimization and Robustness in Dynamic Systems

Author

Listed:
  • Xin Zhou

    (College of Computer Science and Engineering, Jishou University, Jishou 416000, China)

  • Bolin Liao

    (College of Computer Science and Engineering, Jishou University, Jishou 416000, China)

Abstract

Zeroing Neural Networks (ZNNs), an ODE-based neural dynamics framework, has become a pivotal approach for solving time-varying problems in dynamic systems. This paper systematically reviews recent advances in improving the convergence of ZNN models, focusing on the optimization of fixed parameters, dynamic parameters, and activation functions. Additionally, structural adaptations and fuzzy control strategies have significantly enhanced the robustness and disturbance rejection capabilities of these systems. ZNNs have been successfully applied in robotic control, demonstrating superior accuracy and robustness compared to traditional methods. Future research directions include exploring nonlinear activation functions, multimodal adaptation strategies, and scalability in real-world environments.

Suggested Citation

  • Xin Zhou & Bolin Liao, 2025. "Advances in Zeroing Neural Networks: Convergence Optimization and Robustness in Dynamic Systems," Mathematics, MDPI, vol. 13(11), pages 1-25, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1801-:d:1666530
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/11/1801/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/11/1801/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:13:y:2025:i:11:p:1801-:d:1666530. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.