Meteorological data analysis using extreme learning machines; Proceedings of SPIE; Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics
| Parent link: | Proceedings of SPIE.— .— Bellingham: SPIE Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics.— 2023.— 1278073, 4 p. |
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| Awduron Eraill: | , , , |
| Crynodeb: | Title screen A practical study of statistical modelling language packages R has been carried out using regularization algorithms, more precisely one of the algorithms called the Extreme Learning Machine (ELM). Due to its simple implementation, ELM requires less researcher intervention in setting its parameters. At the same time, the generalization performance of ELM is not sensitive to the dimensionality of the feature space (the number of hidden nodes). Even on a medium-power personal computer, this class of neural networks has made it possible to perform numerous experiments on model building, forecasting and identifying cause-effect relationships in meteorological time series, downloaded from the climate monitoring system of IMCES SB RAS in a reasonable amount of time Текстовый файл AM_Agreement |
| Iaith: | Saesneg |
| Cyhoeddwyd: |
2023
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| Mynediad Ar-lein: | https://doi.org/10.1117/12.2690069 Статья на русском языке |
| Fformat: | Electronig Pennod Llyfr |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680002 |
| Crynodeb: | Title screen A practical study of statistical modelling language packages R has been carried out using regularization algorithms, more precisely one of the algorithms called the Extreme Learning Machine (ELM). Due to its simple implementation, ELM requires less researcher intervention in setting its parameters. At the same time, the generalization performance of ELM is not sensitive to the dimensionality of the feature space (the number of hidden nodes). Even on a medium-power personal computer, this class of neural networks has made it possible to perform numerous experiments on model building, forecasting and identifying cause-effect relationships in meteorological time series, downloaded from the climate monitoring system of IMCES SB RAS in a reasonable amount of time Текстовый файл AM_Agreement |
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| DOI: | 10.1117/12.2690069 |