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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Detaylı Bibliyografya
Asıl Yazarlar: Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: Springer 2024-03-01
Seri Bilgileri:Industrial Artificial Intelligence
Konular:
Recurrent neural networks
Stochastic configuration networks
Incremental learning
Time series
Deep learning
Online Erişim:https://doi.org/10.1007/s44244-024-00017-7

Internet

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

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