IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v531y2019ics037843711931043x.html
   My bibliography  Save this article

Finite-time projective synchronization of memristor-based neural networks with leakage and time-varying delays

Author

Listed:
  • Qin, Xiaoli
  • Wang, Cong
  • Li, Lixiang
  • Peng, Haipeng
  • Yang, Yixian
  • Ye, Lu

Abstract

This paper is concerned with the finite-time projective synchronization problem of memristor-based neural networks(MNNs) with leakage and time-varying delays. The finite-time modified projective synchronization and function projective synchronization theorems are proposed, and the approach of Lyapunov stability and two different finite-time synchronization methods are adopted in the proof processes. Based on time-delay correlation and irrelevant problem, two different controllers are designed, and several stability conditions are presented to ensure that the drive–response systems achieve the finite-time synchronization with arbitrary continuous bounded functions. Meanwhile, several corollaries about the special cases of finite-time projective synchronization are given along with the theorems. Finally, two numerical simulations are carried out to illustrate the effectiveness and verify our results.

Suggested Citation

  • Qin, Xiaoli & Wang, Cong & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Ye, Lu, 2019. "Finite-time projective synchronization of memristor-based neural networks with leakage and time-varying delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s037843711931043x
    DOI: 10.1016/j.physa.2019.121788
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711931043X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.121788?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. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    2. Shuang Wang & Hai Zhang & Weiwei Zhang & Hongmei Zhang, 2021. "Finite-Time Projective Synchronization of Caputo Type Fractional Complex-Valued Delayed Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-14, June.
    3. He, Jin-Man & Pei, Li-Jun, 2023. "Function matrix projection synchronization for the multi-time delayed fractional order memristor-based neural networks with parameter uncertainty," Applied Mathematics and Computation, Elsevier, vol. 454(C).
    4. Pu, Hao & Li, Fengjun, 2023. "Fixed/predefined-time synchronization of complex-valued discontinuous delayed neural networks via non-chattering and saturation control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    5. Liu, Dan & Wang, Zidong & Liu, Yurong & Alsaadi, Fuad E., 2021. "Recursive filtering for stochastic parameter systems with measurement quantizations and packet disorders," Applied Mathematics and Computation, Elsevier, vol. 398(C).

    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:phsmap:v:531:y:2019:i:c:s037843711931043x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.