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dc.contributor.authorSulla-Torres, José
dc.contributor.authorLuna-Luza, Gonzalo
dc.contributor.authorCcama-Yana, Doris
dc.contributor.authorGallegos-Valdivia, Juan
dc.contributor.authorCossio-Bolaños, Marco Antonio
dc.date.accessioned2020-11-12T13:48:23Z
dc.date.available2020-11-12T13:48:23Z
dc.date.issued2020
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/3188
dc.description.abstractPhysical fitness is widely known to be one of the critical elements of a healthy life. The sedentary attitude of school children is related to some health problems due to physical inactivity. The following article aims to classify the physical fitness in school children, using a database of 1813 children of both sexes, in a range that goes from six to twelve years. The physical tests were flexibility, horizontal jump, and agility that served to classify the physical fitness using neural networks and fuzzy logic. For this, the ANFIS (adaptive network fuzzy inference system) model was used, which was optimized using the Particle Swarm Optimization algorithm. The experimental tests carried out showed an RMSE error of 3.41, after performing 500 interactions of the PSO algorithm. This result is considered acceptable within the conditions of this investigation.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 Journal of Advanced Computer Science and Applications, 11(6)es_CL
dc.subjectANFISes_CL
dc.subjectParticle swarm optimizationes_CL
dc.subjectPhysical fitnesses_CL
dc.subjectRMSEes_CL
dc.titleNeuro-fuzzy system with particle swarm optimization for classification of physical fitness in school childrenes_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Educaciónes_CL
dc.ucm.indexacionScopuses_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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