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dc.creatorOliveira, Lizandro de Souzaes_ES
dc.creatorYamin, Adenaueres_ES
dc.creatorReiser, Renataes_ES
dc.creatorSantos, Helidaes_ES
dc.date.accessioned2023-03-03T12:24:19Z
dc.date.available2023-03-03T12:24:19Z
dc.date.issued2022
dc.identifier.citationSouza Oliveira, L. D., Correa Yamin, A., Sander Reiser, R. H., & Salles Santos, H. (2022). F-hybridmem: A consensual analysis via fuzzy consensus measures and penalty functions. 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8. https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882879en
dc.identifier.isbn978-1-6654-6710-0
dc.identifier.urihttps://hdl.handle.net/2454/44831
dc.description.abstractThis paper considers the consensual analysis in decision-making (CDM) processes based on fuzzy logic (FL) and interval-valued fuzzy logic (IVFL), providing a CDM-strategy, by exploring the axiomatic properties of fuzzy consensus measures (FCM) via penalty functions. Thus, two models are formalized, FS-FCM and IVFS-FCM. In the former, the fuzzy-valued lattice enables the analysis of fuzzy information for linguistic variables (LV), which is obtained by the aggregation of penalty functions. And, in the latter, the consensus measures of fuzzy sets are aggregated to build a new consensual analysis modeling. Thus, e.g., the cohesion of several terms related to the same LV can be analyzed, and also the coherence between fuzzy sets referring to the lowest and highest projections. Such models decide based on relevance criteria and qualitative assessments, via the selection of alternatives, supporting the corresponding algorithmic strategies: FS-FCM strategy, applied to fuzzy values, and IVFS-FCM strategy, covering fuzzy sets. The Intf-HybridMem approach explores the access patterns to volatile and non-volatile memories related to decision-making in two steps: (i) the FS-FCM strategy explores consensus measures of fuzzy values from membership functions; and (ii) the IVFS-FCM strategy, modeling inaccuracy inherent in input variables, as read/write frequency and access recency, also including the migration recommendation as output, which is validated by evaluations carried out in both proposed strategies.en
dc.description.sponsorshipThis work was partially supported by CAPES, PQ/CNPq (309160/2019-7), PqG/FAPERGS (21/2551-0002057-1) and FAPERGS/CNPq PRONEX (16/2551-0000488-9).en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2022 p.1-8en
dc.rights© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worken
dc.subjectDecision Making Problemen
dc.subjectFuzzy Consensus Measureen
dc.subjectHybrid Memory Managementen
dc.subjectPenalty Functionsen
dc.titlef-HybridMem: a consensual analysis via fuzzy consensus measures and penalty functionsen
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.date.updated2023-03-03T09:54:26Z
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1109/FUZZ-IEEE55066.2022.9882879
dc.relation.publisherversionhttps://doi.org/10.1109/FUZZ-IEEE55066.2022.9882879
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen


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