Redes neuronales artificiales en el pronóstico de la producción de leche bovina

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dc.contributor.authorPerdigón Llanes, Rudibel
dc.contributor.authorGonzález Benítez, Neilys
dc.contributor.orcidPerdigón Llanes, Rudibel [0000-0001-7288-6224]spa
dc.contributor.orcidGonzález Benítez, Neilys [0000-0001-8691-445X]spa
dc.date.accessioned2024-09-13T20:54:33Z
dc.date.available2024-09-13T20:54:33Z
dc.date.issued2021-11-23
dc.description.abstractLos pronósticos facilitan la toma de decisiones en granjas productoras de leche y contribuyen a mejorar la cadena productiva de este alimento. En la literatura se identificó que las redes neuronales artificiales poseen un ajuste aceptable al pronóstico de las producciones de leche. Sin embargo, en las fuentes bibliográficas consultadas no se evidenció un consenso sobre el tipo de red neuronal artificial con mejores rendimientos en esta actividad. Esta investigación tiene como objetivo identificar la red neuronal artificial con mayores índices de desempeño en el pronóstico de la producción de leche bovina. Se realizó una revisión de la literatura relacionada con los pronósticos de las producciones de leche mediante el uso de redes neuronales artificiales. Los resultados obtenidos en la literatura analizada evidenciaron que las redes no lineales autorregresivas con variables exógenas y las redes convolucionales poseen los mejores rendimientos en el pronóstico de la producción de leche bovina.spa
dc.description.abstractenglishForecasting facilitates decision-making on dairy farms and contributes to improving the milk production chain. According to the literature, artificial neural networks have an acceptable adjustment to milk production forecasting. However, in the consulted bibliographic sources, there was no consensus on the type of artificial neural network with the best performance in this activity. This research is aimed at identifying the artificial neural network with the highest performance levels in bovine milk production forecasting. A literature review related to milk production forecasting using artificial neural networks was carried out. The results from the sources examined revealed that nonlinear autoregressive networks with exogenous variables and convolutional neural networks perform best in forecasting bovine milk production.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.29375/25392115.4209
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.identifier.issnISSN: 1657-2831spa
dc.identifier.issne-ISSN: 2539-2115spa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/26524
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/4209/3609spa
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dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/issue/view/282spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourceVol. 23 Núm. 1 (2022): Revista Colombiana de Computación (Enero-Junio); 20-33spa
dc.subjectInteligencia artificialspa
dc.subjectModelos de pronósticospa
dc.subjectGanaderíaspa
dc.subjectToma de decisionesspa
dc.subject.keywordsArtificial intelligenceeng
dc.subject.keywordsForecasting modelseng
dc.subject.keywordsLivestockeng
dc.subject.keywordsDecision makingeng
dc.titleRedes neuronales artificiales en el pronóstico de la producción de leche bovinaspa
dc.title.translatedArtificial neural networks in bovine milk production forecastingeng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ART

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