PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections

Detalles Bibliográficos
Autores: Divasón, Jose [0000-0002-5173-128X], Ceniceros, Julio Fernandez [0000-0003-3620-5991], Sanz-Garcia, Andres [0000-0003-0413-4965], Pernia-Espinoza, Alpha [0000-0001-6227-075X], Martinez-de-Pison, Francisco Javier [0000-0002-3063-7374]
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad de La Rioja (UR)
Repositorio:RIUR. Repositorio Institucional de la Universidad de La Rioja
OAI Identifier:oai:portal.dialnet.es:doc/64995b3671c692789f1df3d0
Acceso en línea:https://investigacion.unirioja.es/documentos/64995b3671c692789f1df3d0
Access Level:acceso abierto
Palabra clave:Auto machine learning
GA-PARSIMONY
Parsimonious modeling
PSO-PARSIMONY
t-stub connections
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repository_id_str
spelling PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connectionsDivasón, Jose [0000-0002-5173-128X]Ceniceros, Julio Fernandez [0000-0003-3620-5991]Sanz-Garcia, Andres [0000-0003-0413-4965]Pernia-Espinoza, Alpha [0000-0001-6227-075X]Martinez-de-Pison, Francisco Javier [0000-0002-3063-7374]Auto machine learningGA-PARSIMONYParsimonious modelingPSO-PARSIMONYt-stub connectionsElsevier B.V.2023info:eu-repo/semantics/articleSubtype: Articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://investigacion.unirioja.es/documentos/64995b3671c692789f1df3d0reponame:RIUR. Repositorio Institucional de la Universidad de La Riojainstname:Universidad de La Rioja (UR)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/J.NEUCOM.2023.126414info:eu-repo/semantics/altIdentifier/eissn/1872-8286info:eu-repo/semantics/altIdentifier/pissn/0925-2312PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections, 2023, vol. 548info:eu-repo/semantics/openAccessoai:portal.dialnet.es:doc/64995b3671c692789f1df3d02026-06-14T12:47:17Z
dc.title.none.fl_str_mv PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
title PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
spellingShingle PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
Divasón, Jose [0000-0002-5173-128X]
Auto machine learning
GA-PARSIMONY
Parsimonious modeling
PSO-PARSIMONY
t-stub connections
title_short PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
title_full PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
title_fullStr PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
title_full_unstemmed PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
title_sort PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
dc.creator.none.fl_str_mv Divasón, Jose [0000-0002-5173-128X]
Ceniceros, Julio Fernandez [0000-0003-3620-5991]
Sanz-Garcia, Andres [0000-0003-0413-4965]
Pernia-Espinoza, Alpha [0000-0001-6227-075X]
Martinez-de-Pison, Francisco Javier [0000-0002-3063-7374]
author Divasón, Jose [0000-0002-5173-128X]
author_facet Divasón, Jose [0000-0002-5173-128X]
Ceniceros, Julio Fernandez [0000-0003-3620-5991]
Sanz-Garcia, Andres [0000-0003-0413-4965]
Pernia-Espinoza, Alpha [0000-0001-6227-075X]
Martinez-de-Pison, Francisco Javier [0000-0002-3063-7374]
author_role author
author2 Ceniceros, Julio Fernandez [0000-0003-3620-5991]
Sanz-Garcia, Andres [0000-0003-0413-4965]
Pernia-Espinoza, Alpha [0000-0001-6227-075X]
Martinez-de-Pison, Francisco Javier [0000-0002-3063-7374]
author2_role author
author
author
author
dc.subject.none.fl_str_mv Auto machine learning
GA-PARSIMONY
Parsimonious modeling
PSO-PARSIMONY
t-stub connections
topic Auto machine learning
GA-PARSIMONY
Parsimonious modeling
PSO-PARSIMONY
t-stub connections
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
Subtype: Article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://investigacion.unirioja.es/documentos/64995b3671c692789f1df3d0
url https://investigacion.unirioja.es/documentos/64995b3671c692789f1df3d0
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/J.NEUCOM.2023.126414
info:eu-repo/semantics/altIdentifier/eissn/1872-8286
info:eu-repo/semantics/altIdentifier/pissn/0925-2312
PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections, 2023, vol. 548
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:RIUR. Repositorio Institucional de la Universidad de La Rioja
instname:Universidad de La Rioja (UR)
instname_str Universidad de La Rioja (UR)
reponame_str RIUR. Repositorio Institucional de la Universidad de La Rioja
collection RIUR. Repositorio Institucional de la Universidad de La Rioja
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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