Universal approximation property of stochastic configuration networks for time series

Universal approximation property of stochastic configuration networks for time series

Abstract For the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic Configuration Network (RSCN). This network is...

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Bibliografski detalji
Glavni autori: Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
Format: Članak
Jezik:English
Izdano: Springer 2024-03-01
Serija:Industrial Artificial Intelligence
Teme:
Recurrent neural networks
Stochastic configuration networks
Incremental learning
Time series
Deep learning
Online pristup:https://doi.org/10.1007/s44244-024-00017-7

Internet

https://doi.org/10.1007/s44244-024-00017-7

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