IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3542898.html
   My bibliography  Save this article

A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

Author

Listed:
  • Qinghai Li
  • Rui-Chang Lin

Abstract

A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature. In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN). This novel network does not increase complexity in fuzzy inference or learning process. Specifically, the structure learning is based on partition of the input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This novel network can also be applied for chaotic time series prediction including Logistic and Henon time series. More significantly, it features rapider convergence and higher prediction accuracy.

Suggested Citation

  • Qinghai Li & Rui-Chang Lin, 2016. "A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, December.
  • Handle: RePEc:hin:jnlmpe:3542898
    DOI: 10.1155/2016/3542898
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2016/3542898.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2016/3542898.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2016/3542898?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. Miao, Hua & Zhu, Wei & Dan, Yuanhong & Yu, Nanxiang, 2024. "Chaotic time series prediction based on multi-scale attention in a multi-agent environment," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    2. Sangiorgio, Matteo & Dercole, Fabio & Guariso, Giorgio, 2021. "Forecasting of noisy chaotic systems with deep neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).

    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:jnlmpe:3542898. 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.