Sequence Prediction and Classification of Echo State Networks

Jingyu Sun, Lixiang Li () and Haipeng Peng
Additional contact information
Jingyu Sun: Information Security Center, State Key Laboratory of Networking and Switching Technology, 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
Haipeng Peng: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Mathematics, 2023, vol. 11, issue 22, 1-14

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.

Keywords: echo state network; sequence production; sequence classification; network security; intelligent computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/22/4640/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/22/4640/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:22:p:4640-:d:1279663

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().