IDEAS home Printed from https://ideas.repec.org/a/hin/complx/7476250.html
   My bibliography  Save this article

Predefined-Time Antisynchronization of Two Different Chaotic Neural Networks

Author

Listed:
  • Lixiong Lin

Abstract

This paper is concerned with antisynchronization in predefined time for two different chaotic neural networks. Firstly, a predefined-time stability theorem based on Lyapunov function is proposed. With the help of the definition of predefined time, it is convenient to establish a direct relationship between the tuning gain of the system and the fixed stabilization time. Then, the antisynchronization is achieved between two different chaotic neural networks via active control Lyapunov function design. The designed controller presents the practical advantage that the least upper bound for the settling time can be explicitly defined during the control design. With the help of the designed controller, the antisynchronization errors converge within a predefined-time period. Numerical simulations are presented in order to show the reliability of the proposed method.

Suggested Citation

  • Lixiong Lin, 2020. "Predefined-Time Antisynchronization of Two Different Chaotic Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-11, June.
  • Handle: RePEc:hin:complx:7476250
    DOI: 10.1155/2020/7476250
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/7476250.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2020/7476250.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/7476250?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Guodong & Cao, Jinde, 2023. "New results on fixed/predefined-time synchronization of delayed fuzzy inertial discontinuous neural networks: Non-reduced order approach," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    2. Wang, Shasha & Jian, Jigui, 2023. "Predefined-time synchronization of fractional-order memristive competitive neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Wang, Shasha & Jian, Jigui, 2023. "Predefined-time synchronization of incommensurate fractional-order competitive neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

    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:complx:7476250. 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.