IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v196y2025ics0960077925004163.html
   My bibliography  Save this article

Global polynomial synchronization for quaternion-valued T–S fuzzy inertial neural networks via event-triggered control: A polynomial gain method

Author

Listed:
  • Zhang, Jingjing
  • Li, Zhouhong
  • Cao, Jinde
  • Abdel-Aty, Mahmoud
  • Meng, Xiaofang

Abstract

This work explores the global polynomial synchronization for a class of quaternion-valued Takagi–Sugeno fuzzy inertial neural networks based on event-triggered control. Firstly, the paper designs the fuzzy event-triggered controller with a polynomial gain, a unique approach to optimize the event-triggered mechanism. The non-reduced order and non-decomposition methods are applied to maintain computational efficiency without introducing new variables. Then, under static and dynamic event-triggered conditions, the system’s global polynomial synchronization is guaranteed by formulating a suitable delay-free Lyapunov functional and using quaternion properties and inequality techniques. Moreover, rigorous derivation is employed to verify a positive lower bound of any event-triggered interval, concluding that the system does not produce Zeno behavior. Finally, a numerical example and the application of image encryption and decryption are presented to strongly validate the reliability of the model and control mechanism in achieving global polynomial synchronization.

Suggested Citation

  • Zhang, Jingjing & Li, Zhouhong & Cao, Jinde & Abdel-Aty, Mahmoud & Meng, Xiaofang, 2025. "Global polynomial synchronization for quaternion-valued T–S fuzzy inertial neural networks via event-triggered control: A polynomial gain method," Chaos, Solitons & Fractals, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:chsofr:v:196:y:2025:i:c:s0960077925004163
    DOI: 10.1016/j.chaos.2025.116403
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925004163
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2025.116403?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:eee:chsofr:v:196:y:2025:i:c:s0960077925004163. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-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.