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...

書誌詳細
主要な著者:	Jin-Xi Zhang, Hangyi Zhao, Xuefeng Zhang
フォーマット:	論文
言語:	English
出版事項:	Springer 2024-03-01
シリーズ:	Industrial Artificial Intelligence
主題:	Recurrent neural networks Stochastic configuration networks Incremental learning Time series Deep learning
オンライン･アクセス:	https://doi.org/10.1007/s44244-024-00017-7

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