Meteorological data analysis using extreme learning machines; Proceedings of SPIE; Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics

Meteorological data analysis using extreme learning machines; Proceedings of SPIE; Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics

Manylion Llyfryddiaeth
Parent link:	Proceedings of SPIE.— .— Bellingham: SPIE Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics.— 2023.— 1278073, 4 p.
Awduron Eraill:	Botygin I. A. Igor Aleksandrovich, Volkov Yu. V., Sherstnev V. S. Vladislav Stanislavovich, Sherstneva A. I. Anna Igorevna
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
Pynciau:	feedforward neural networks machine learning extreme learning machine multilayer neural network электронный ресурс труды учёных ТПУ
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

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