Meteorological data analysis using extreme learning machines
| Parent link: | Proceedings of SPIE.— .— Bellingham: SPIE |
|---|---|
| מחברים אחרים: | , , , |
| סיכום: | 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 |
| שפה: | אנגלית |
| יצא לאור: |
2023
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| נושאים: | |
| גישה מקוונת: | https://doi.org/10.1117/12.2690069 Статья на русском языке |
| פורמט: | אלקטרוני Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680002 |
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| 200 | 1 | |a Meteorological data analysis using extreme learning machines |d Анализ метеорологических данных с использованием экстремальных самообучающихся машин |z rus |f I. A. Botygin, Yu. V. Volkov, V. S. Sherstnev, A. I. Sherstneva | |
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| 330 | |a 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 | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |0 646891 |9 646891 |t Proceedings of SPIE |c Bellingham |n SPIE | |
| 463 | 1 | |t Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics |l Оптика атмосферы и океана. Физика атмосферы |o proceedings 29th International Symposium, 26-30 June 2023 Moscow, Russian Federation |o материалы XXIX Международного симпозиума, 26-30 июня 2023 года, Москва |f Institute of Atmospheric Optics SB RAS ; eds. O. A. Romanovskii |v 1278073, 4 p. |d 2023 | |
| 610 | 1 | |a feedforward neural networks | |
| 610 | 1 | |a machine learning | |
| 610 | 1 | |a extreme learning machine | |
| 610 | 1 | |a multilayer neural network | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 701 | 1 | |a Botygin |b I. A. |c specialist in the field of Informatics and computer engineering |c Associate Professor of Tomsk Polytechnic University, candidate of technical sciences |f 1947- |g Igor Aleksandrovich |9 17356 | |
| 701 | 1 | |a Volkov |b Yu. V. | |
| 701 | 1 | |a Sherstnev |b V. S. |c specialist in the field of Informatics and computer engineering |c associate Professor of Tomsk Polytechnic University, candidate of technical Sciences |f 1974- |g Vladislav Stanislavovich |9 17137 | |
| 701 | 1 | |a Sherstneva |b A. I. |c mathematician |c associate Professor of Tomsk Polytechnic University, candidate of physico-mathematical Sciences |f 1974- |g Anna Igorevna |9 18721 | |
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| 856 | 4 | |u https://doi.org/10.1117/12.2690069 |z https://doi.org/10.1117/12.2690069 | |
| 856 | 4 | |u https://symp-pv.iao.ru/files/symp/aoo/29/E.pdf#page=62 |z Статья на русском языке | |
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