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dc.creatorPalmeira, Eduardo S.es_ES
dc.creatorBedregal, Benjamines_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorPaternain Dallo, Danieles_ES
dc.creatorMiguel Turullols, Laura dees_ES
dc.date.accessioned2018-02-20T10:17:37Z
dc.date.available2020-05-01T23:00:13Z
dc.date.issued2018
dc.identifier.issn0020-0255 (Print)
dc.identifier.issn1872-6291 (Electronic)
dc.identifier.urihttps://hdl.handle.net/2454/27301
dc.description.abstractBased on previous investigations, we have proposed two different methods to extend lattice-valued fuzzy connectives (t-norms, t-conorms, negations and implications) and other related operators, considering a generalized notion of sublattices. Taking into account the results obtained and seeking to analyze the behavior of both extension methods in face of fuzzy operators related to image processing, we have applied these methods so as to extend restricted equivalence functions, restricted dissimilarity functions and Ee,N-normal functions. We also generalize the concepts of similarity measure, distance measure and entropy measure for L-fuzzy sets constructing them via restricted equivalence functions, restricted dissimilarity functions and Ee,N-normal functionsen
dc.description.sponsorshipThis work was partially supported by the Brazilian Funding Agency CNPq under the Process 307781/2016-0, the Research Services of Universidad Publica de Navarra and by the research project TIN2016-77356-P from MINECO, AEI/FEDER, UE.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofInformation Sciences, 441 (2018) 95–112en
dc.rights© 2018 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 licenseen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFuzzy logicen
dc.subjectRestricted equivalence functionsen
dc.subjectRetractionsen
dc.subjectExtensionen
dc.subjectE-operatorsen
dc.titleApplication of two different methods for extending lattice-valued restricted equivalence functions used for constructing similarity measures on L-fuzzy setsen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentAutomatika eta Konputazioaeu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.departmentAutomática y Computaciónes_ES
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.embargo.terms2020-05-01
dc.identifier.doi10.1016/j.ins.2018.02.022
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-Pen
dc.relation.publisherversionhttps://doi.org/10.1016/j.ins.2018.02.022
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes


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© 2018 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license
La licencia del ítem se describe como © 2018 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license

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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