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dc.creatorFerrero Jaurrieta, Mikeles_ES
dc.creatorPereira Dimuro, Graçalizes_ES
dc.creatorTakáč, Zdenkoes_ES
dc.creatorSantiago, Regivanes_ES
dc.creatorFernández Fernández, Francisco Javieres_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.date.accessioned2022-03-10T07:59:59Z
dc.date.available2022-03-10T07:59:59Z
dc.date.issued2021
dc.identifier.issn1613-0073
dc.identifier.urihttps://hdl.handle.net/2454/42468
dc.description.abstractGated Recurrent Units (GRU) are neural network gated architectures that simplify other ones (suchas, LSTM) by joining gates mainly. For this, instead of using two gates, if𝑥is the first gate, standardoperation1−𝑥is used to generate the second one, optimizing the number of parameters. In this work, we interpret this information as a fuzzy set, and we generalize the standard operation using fuzzy negations, and improving the accuracy obtained with the standard one.en
dc.description.sponsorshipGrant PID2019-108392GB-I00 funded by MCIN/AEI/10.13039/501100011033 and by Tracasa Instrumental and the Immigration Policy and Justice Department of the Government of Navarre.en
dc.format.extent9 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherCEUR Workshop Proceedings (CEUR-WS.org)en
dc.relation.ispartofWILF’21: The 13th International Workshop on Fuzzy Logic and Applications, Dec. 20–22, 2021, Vietri sul Mare, Italyen
dc.rights© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFuzzy set complementen
dc.subjectFuzzy negationsen
dc.subjectRecurrent neural networksen
dc.subjectGated recurrent uniten
dc.titleFuzzy sets complement-based gated recurrent uniten
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/en
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.contributor.funderGobierno de Navarra / Nafarroako Gobernuaes


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© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
La licencia del ítem se describe como © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
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