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...
| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Springer
2024-03-01
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| シリーズ: | Industrial Artificial Intelligence |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/s44244-024-00017-7 |