IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v483y2017icp351-361.html
   My bibliography  Save this article

Synchronization-based parameter estimation of fractional-order neural networks

Author

Listed:
  • Gu, Yajuan
  • Yu, Yongguang
  • Wang, Hu

Abstract

This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Gu, Yajuan & Yu, Yongguang & Wang, Hu, 2017. "Synchronization-based parameter estimation of fractional-order neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 351-361.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:351-361
    DOI: 10.1016/j.physa.2017.04.124
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117304405
    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.2017.04.124?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.

    References listed on IDEAS

    as
    1. Wang, Sha & Yu, Yongguang & Diao, Miao, 2010. "Hybrid projective synchronization of chaotic fractional order systems with different dimensions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4981-4988.
    2. Peng, Guojun & Jiang, Yaolin & Chen, Fang, 2008. "Generalized projective synchronization of fractional order chaotic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3738-3746.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zheng, Yi & Wu, Xiaoqun & Fan, Ziye & Wang, Wei, 2022. "Identifying topology and system parameters of fractional-order complex dynamical networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deepika, Deepika & Kaur, Sandeep & Narayan, Shiv, 2018. "Uncertainty and disturbance estimator based robust synchronization for a class of uncertain fractional chaotic system via fractional order sliding mode control," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 196-203.
    2. Kingni, Sifeu Takougang & Pham, Viet-Thanh & Jafari, Sajad & Woafo, Paul, 2017. "A chaotic system with an infinite number of equilibrium points located on a line and on a hyperbola and its fractional-order form," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 209-218.
    3. He, Shaobo & Banerjee, Santo & Sun, Kehui, 2018. "Can derivative determine the dynamics of fractional-order chaotic system?," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 14-22.
    4. Liu, Hui & Chen, Juan & Lu, Jun-an & Cao, Ming, 2010. "Generalized synchronization in complex dynamical networks via adaptive couplings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1759-1770.
    5. Wang, Sha & Yu, Yongguang & Diao, Miao, 2010. "Hybrid projective synchronization of chaotic fractional order systems with different dimensions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4981-4988.
    6. Li, Hang & Shen, Yongjun & Han, Yanjun & Dong, Jinlu & Li, Jian, 2023. "Determining Lyapunov exponents of fractional-order systems: A general method based on memory principle," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    7. Karthikeyan Rajagopal & Laarem Guessas & Anitha Karthikeyan & Ashokkumar Srinivasan & Girma Adam, 2017. "Fractional Order Memristor No Equilibrium Chaotic System with Its Adaptive Sliding Mode Synchronization and Genetically Optimized Fractional Order PID Synchronization," Complexity, Hindawi, vol. 2017, pages 1-19, March.

    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:483:y:2017:i:c:p:351-361. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.