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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מידע ביבליוגרפי
Main Authors:	Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
פורמט:	Article
שפה:	English
יצא לאור:	Springer 2024-03-01
סדרה:	Industrial Artificial Intelligence
נושאים:	Recurrent neural networks Stochastic configuration networks Incremental learning Time series Deep learning
גישה מקוונת:	https://doi.org/10.1007/s44244-024-00017-7

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