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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
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    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).
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