IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v51y2020i2p303-312.html
   My bibliography  Save this article

Sampled-data state-estimation of delayed complex-valued neural networks

Author

Listed:
  • Nallappan Gunasekaran
  • Guisheng Zhai

Abstract

This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.

Suggested Citation

  • Nallappan Gunasekaran & Guisheng Zhai, 2020. "Sampled-data state-estimation of delayed complex-valued neural networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(2), pages 303-312, January.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:2:p:303-312
    DOI: 10.1080/00207721.2019.1704095
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2019.1704095
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2019.1704095?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.

    Citations

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


    Cited by:

    1. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Global Stability Analysis of Fractional-Order Quaternion-Valued Bidirectional Associative Memory Neural Networks," Mathematics, MDPI, vol. 8(5), pages 1-27, May.
    2. Yaning Yu & Ziye Zhang, 2022. "State Estimation for Complex-Valued Inertial Neural Networks with Multiple Time Delays," Mathematics, MDPI, vol. 10(10), pages 1-14, May.
    3. Yihui Lei & Zhengqi Dai & Bolin Liao & Guangping Xia & Yongjun He, 2022. "Double Features Zeroing Neural Network Model for Solving the Pseudoninverse of a Complex-Valued Time-Varying Matrix," Mathematics, MDPI, vol. 10(12), pages 1-19, June.

    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:taf:tsysxx:v:51:y:2020:i:2:p:303-312. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.