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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Bibliografiska uppgifter
Huvudupphovsmän:	Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
Materialtyp:	Artikel
Språk:	English
Publicerad:	Springer 2024-03-01
Serie:	Industrial Artificial Intelligence
Ämnen:	Recurrent neural networks Stochastic configuration networks Incremental learning Time series Deep learning
Länkar:	https://doi.org/10.1007/s44244-024-00017-7

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