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dc.contributor.authorDivasón, Jose
dc.contributor.authorCeniceros, Julio Fernandez
dc.contributor.authorSanz García, Andrés 
dc.contributor.authorPernía-Espinoza, Alpha
dc.contributor.authorMartinez-de-Pison, Francisco Javier
dc.date.accessioned2024-05-10T09:00:46Z
dc.date.available2024-05-10T09:00:46Z
dc.date.issued2023-09
dc.identifier.citationDivasón, J., Ceniceros, J. F., Sanz-Garcia, A., Pernia-Espinoza, A., & Martinez-de-Pison, F. J. (2023). 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. Neurocomputing, 548, 126414. https://doi.org/10.1016/j.neucom.2023.126414es_ES
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/10366/157803
dc.description.abstract[EN]We present PSO-PARSIMONY, a new methodology to search for parsimonious and highly accurate models by means of particle swarm optimization. PSO-PARSIMONY uses automatic hyperparameter optimization and feature selection to search for accurate models with low complexity. To evaluate the new proposal, a comparative study with multilayer perceptron algorithm was performed with public datasets and by applying it to predict two important parameters of the force–displacement curve in T-stub steel connections: initial stiffness and maximum strength. Models optimized with PSO-PARSIMONY showed an excellent trade-off between goodness-of-fit and parsimony. The new proposal was compared with GA-PARSIMONY, our previously published methodology that uses genetic algorithms in the optimization process. The new method needed more iterations and obtained slightly more complex individuals, but it performed better in the search for accurate models.es_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.subjectPSO-PARSIMONYes_ES
dc.subjectt-stub connectionses_ES
dc.subjectParsimonious modelinges_ES
dc.subjectAuto machine learninges_ES
dc.subjectGA-PARSIMONYes_ES
dc.titlePSO-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.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttps://doi.org/10.1016/j.neucom.2023.126414es_ES
dc.relation.projectIDPID2020-116641 GB-I00es_ES
dc.relation.projectIDPID2021-123219OB-I00es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleNeurocomputinges_ES
dc.volume.number548es_ES
dc.page.initial126414es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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