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

Bibliographic Details
Parent link:Proceedings of SPIE.— .— Bellingham: SPIE
Vol. 12780 : Atmospheric and Ocean Optics: Atmospheric Physics.— 2023.— 1278073, 4 p.
Other Authors: Botygin I. A. Igor Aleksandrovich, Volkov Yu. V., Sherstnev V. S. Vladislav Stanislavovich, Sherstneva A. I. Anna Igorevna
Summary: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
Language:English
Published: 2023
Subjects:
feedforward neural networks
machine learning
extreme learning machine
multilayer neural network
электронный ресурс
труды учёных ТПУ
Online Access:https://doi.org/10.1117/12.2690069
Статья на русском языке
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680002

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