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dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorOlga Koshelevaen_US
dc.date.accessioned2018-09-04T09:49:07Z-
dc.date.available2018-09-04T09:49:07Z-
dc.date.issued2014-01-01en_US
dc.identifier.issn10987584en_US
dc.identifier.other2-s2.0-84912529656en_US
dc.identifier.other10.1109/FUZZ-IEEE.2014.6891598en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84912529656&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/53432-
dc.description.abstract© 2014 IEEE. In the traditional fuzzy logic, the expert's degree of confidence d(A&B) in a complex statement A&B (or A V B) is uniquely determined by his/her degrees of confidence d(A) and d(B) in the statements A and B, as f&(d(A), d(B)) for an appropriate 'and'-operation (t-norm). In practice, for the same degrees d(A) and d(B), we may have different degrees d(A&B) depending on the relation between A and B. The best way to take this relation into account is to explicitly elicit the corresponding degrees d(A&B) and d(A V B), i.e., to come up with a 'double' fuzzy set. If we only elicit information about pairs of statements, then we still need to estimate, e.g., the degree d(A & B & C) based on the known values d(A), d(B), d(C), d(A & B), d(A & C), and d(B & C). In this paper, we explain how to produce such 'and'-operations for 'double' fuzzy sets - and how to produce similar 'or'-operations.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.title"and"- and "or"-operations for "double", "triple", etc. fuzzy setsen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleIEEE International Conference on Fuzzy Systemsen_US
article.stream.affiliationsNew Mexico State University Las Crucesen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
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