IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i22p4640-d1279663.html
   My bibliography  Save this article

Sequence Prediction and Classification of Echo State Networks

Author

Listed:
  • Jingyu Sun

    (Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Lixiang Li

    (Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Haipeng Peng

    (Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China)

Abstract

The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared to traditional neural networks and is highly regarded for its simplicity and efficiency in computation. In recent years, as network development has progressed, the security threats faced by networks have increased. To detect and counter these threats, the analysis of network traffic has become a crucial research focus. The echo state network has demonstrated exceptional performance in sequence prediction. In this article, we delve into the impact of echo state networks on time series. We have enhanced the model by increasing the number of layers and adopting a different data input approach. We apply it to predict chaotic systems that appear ostensibly regular but are inherently irregular. Additionally, we utilize it for the classification of sound sequence data. Upon evaluating the model using root mean squared error and micro-F1, we have observed that our model exhibits commendable accuracy and stability.

Suggested Citation

  • Jingyu Sun & Lixiang Li & Haipeng Peng, 2023. "Sequence Prediction and Classification of Echo State Networks," Mathematics, MDPI, vol. 11(22), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4640-:d:1279663
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/22/4640/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/22/4640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).
    Full references (including those not matched with items on IDEAS)

    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. Zheng, Zihao & Ali, Mumtaz & Jamei, Mehdi & Xiang, Yong & Abdulla, Shahab & Yaseen, Zaher Mundher & Farooque, Aitazaz A., 2023. "Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    2. Mahdavi-Meymand, Amin & Sulisz, Wojciech, 2023. "Application of nested artificial neural network for the prediction of significant wave height," Renewable Energy, Elsevier, vol. 209(C), pages 157-168.

    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:gam:jmathe:v:11:y:2023:i:22:p:4640-:d:1279663. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.