Sistemas híbridos inteligentes basados en lógica difusa y aprendizaje profundo

dc.contributorNúñez Castro, Haydemar María
dc.contributorGiraldo Trujillo, Luis Felipe
dc.contributorLozano Garzón, Carlos Andrés
dc.contributorCOMIT
dc.creatorZapata Conforto, Carlos Leopoldo
dc.date.accessioned2022-06-14T18:17:23Z
dc.date.available2022-06-14T18:17:23Z
dc.date.created2022-06-14T18:17:23Z
dc.date.issued2022-05-10
dc.identifierhttp://hdl.handle.net/1992/57942
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.description.abstractEn el presente trabajo se desarrolló una arquitectura neuro-difusa usando datos de imágenes de satélite de monitoreo de cultivos en el contexto de una sola clase. Para ello, se entrenaron, evaluaron y comprobaron diferentes autocodificadores usando técnicas de deep learning y un clasificador difuso. Aparte de aplicar este enfoque en el contexto de monitoreo de cultivos, se buscó comprobar su aplicación en dos contextos diferentes y comparar su rendimiento contra un autocodificador sin lógica difusa y modelos entrenados con los algoritmos de machine learning Isolation Forest y One Class SVM.
dc.languagespa
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Ingeniería de Información
dc.publisherFacultad de Ingeniería
dc.publisherDepartamento de Ingeniería Sistemas y Computación
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleSistemas híbridos inteligentes basados en lógica difusa y aprendizaje profundo
dc.typeTrabajo de grado - Maestría


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