Ver publicación 

Mostrar el registro sencillo de la publicación

Fast tuning of extreme learning machine neural networks based with simple optimization algorithms

dc.contributor.authorTobar Valenzuela, Luis
dc.contributor.authorMora, Marco
dc.contributor.authorSilva Pavez, Fabián
dc.contributor.authorTorres-Gonzalez, Italo
dc.contributor.authorBarría-Valdebenito, Pedro
dc.date.accessioned2023-03-08T13:27:27Z
dc.date.available2023-03-08T13:27:27Z
dc.date.issued2022
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/4492
dc.description.abstractExtreme Learning Machine (ELM) is a neural network training paradigm that is characterized by simplicity, speed and high level of accuracy. The tuning of the network parameters is normally carried out with non-linear optimization algorithms that break this principle of simplicity and reduced execution time. This article shows that ELM network tuning can be performed efficiently by simple optimization algorithms, consistent with its basic philosophy. Experiments with 8 optimization algorithms are shown, considering 6 widely used databases in training algorithm benchmarks. The numerical results show that the Golden Section Algorithm dramatically reduces the network hyperparameter search time compared to the search while maintaining a high level of accuracy.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceInternational Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 1-5es_CL
dc.subjectTraininges_CL
dc.subjectPhilosophical considerationses_CL
dc.subjectExtreme learning machineses_CL
dc.subjectDatabaseses_CL
dc.subjectNeural networkses_CL
dc.subjectBenchmark testinges_CL
dc.subjectOptimizationes_CL
dc.titleFast tuning of extreme learning machine neural networks based with simple optimization algorithmses_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.uriieeexplore.ieee.org/document/10005961es_CL
dc.ucm.doidoi.org/10.1109/ICA-ACCA56767.2022.10005961es_CL


Ficheros en la publicación

FicherosTamañoFormatoVer

No hay ficheros asociados a esta publicación.

Esta publicación aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo de la publicación

Atribución-NoComercial-SinDerivadas 3.0 Chile
Excepto si se señala otra cosa, la licencia de la publicación se describe como Atribución-NoComercial-SinDerivadas 3.0 Chile