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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
Format: Artykuł
Język:English
Wydane: Springer 2024-03-01
Seria:Industrial Artificial Intelligence
Hasła przedmiotowe:
Recurrent neural networks
Stochastic configuration networks
Incremental learning
Time series
Deep learning
Dostęp online:https://doi.org/10.1007/s44244-024-00017-7

Internet

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

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