Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.

This work is intended to organize a big set of time series of rainfall reanalysis built on the Fourier harmonic that corresponds to the 4.8year cycle of variability. To do that a self-organized map is implemented in four spherical layers trained by recirculation. The methodology is shortly described...

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Autor principal: Huggenberger, Dario Alberto
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
Neural Network, Spherical Self-Organized Maps, Recirculation, Signal Analysis. Phase Evolution, Rainfall Reanalysis, Climate Variability
Acceso en línea:http://hdl.handle.net/20.500.12272/3690
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-3690
record_format dspace
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic Neural Network, Spherical Self-Organized Maps, Recirculation, Signal Analysis. Phase Evolution, Rainfall Reanalysis, Climate Variability
spellingShingle Neural Network, Spherical Self-Organized Maps, Recirculation, Signal Analysis. Phase Evolution, Rainfall Reanalysis, Climate Variability
Huggenberger, Dario Alberto
Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
topic_facet Neural Network, Spherical Self-Organized Maps, Recirculation, Signal Analysis. Phase Evolution, Rainfall Reanalysis, Climate Variability
description This work is intended to organize a big set of time series of rainfall reanalysis built on the Fourier harmonic that corresponds to the 4.8year cycle of variability. To do that a self-organized map is implemented in four spherical layers trained by recirculation. The methodology is shortly described. It is used to organize time series on grid point around the Earth to follow the phase evolution of the signal. The phase and amplitude are the main criterion for organization. It is shown how the successive layers contain more general abstractions, their representativeness around the Globe and in regional scale. The main objective is to show how to use the neural network tool to follow the phase evolution of the signal around the Globe. It is described as an anomaly with highest amplitude in the central Pacific Ocean, this evolution and return after 4.8 years.
format Artículo
publishedVersion
Artículo
author Huggenberger, Dario Alberto
author_facet Huggenberger, Dario Alberto
author_sort Huggenberger, Dario Alberto
title Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
title_short Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
title_full Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
title_fullStr Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
title_full_unstemmed Four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
title_sort four-layer spherical self-organized maps neural networks trained by recirculation to follow the phase evolution of a nearly four-year rainfall signal.
publishDate 2019
url http://hdl.handle.net/20.500.12272/3690
work_keys_str_mv AT huggenbergerdarioalberto fourlayersphericalselforganizedmapsneuralnetworkstrainedbyrecirculationtofollowthephaseevolutionofanearlyfouryearrainfallsignal
bdutipo_str Repositorios
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