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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Dettagli Bibliografici
Autori principali:	Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
Natura:	Articolo
Lingua:	English
Pubblicazione:	Springer 2024-03-01
Serie:	Industrial Artificial Intelligence
Soggetti:	Recurrent neural networks Stochastic configuration networks Incremental learning Time series Deep learning
Accesso online:	https://doi.org/10.1007/s44244-024-00017-7

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