Search results for "Fuzzy relation"

showing 3 items of 3 documents

Involving fuzzy orders for multi-objective linear programming

2012

This paper presents a solution approach for multi-objective linear programming problem. We propose to involve fuzzy order relations to describe the objective functions where in ”classical” fuzzy approach the membership functions which illustrate how far the concrete point is from the solution of individual problem are studied. Further the global fuzzy order relation is constructed by aggregating the individual fuzzy order relations. Thus the global fuzzy relation contains the information about all objective functions and in the last step we find a maximum in the set of constrains with respect to the global fuzzy order relation. We illustrate this approach by an example.

Mathematical optimizationFuzzy classificationMathematics::General MathematicsFuzzy setmulti-objective linear programmingfuzzy order relationType-2 fuzzy sets and systemsDefuzzificationModeling and SimulationFuzzy mathematicsQA1-939aggregation of fuzzy relationsFuzzy numberFuzzy set operationsMathematicsAnalysisMembership functionMathematicsMathematical Modelling and Analysis
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Algorithme pour la résolution des systèmes flous

1978

Sanchez formulated conditions and theoretical methods to resolve fuzzy relations. The purpose of this study is to give an algorithm which would actual- ly enable us to determine the functions of appartenance of unknown relations.

fuzzy relations algorithm fuzzy systems[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and Finance[SHS.ECO]Humanities and Social Sciences/Economics and Finance
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Comparison between statistical and fuzzy approaches for improving diagnostic decision making in patients with chronic nasal symptoms

2014

This paper compares a fuzzy model, expressed in rule-form, with a well known statistical approach (i.e. logistic regression model) for diagnostic decision making in patients with chronic nasal symptoms. The analyses were carried out using a database obtained from a questionnaire administered to 1359 patients with nasal symptoms containing personal data, clinical data and skin prick test (SPT) results. Both the fuzzy model and the logistic regression model developed were validated using a data set obtained from another medical institution. The accuracy of the two models in identifying patients with positive or negative SPT was similar. This study is a preliminary step to the creation of a so…

medicine.medical_specialtyLogistic regression modelSettore MED/09 - Medicina InternaSkin prick testLogicFuzzy inference systemFuzzy modelPrimary careSettore MED/10 - Malattie Dell'Apparato RespiratorioFuzzy relationLogistic regressionMachine learningcomputer.software_genreFuzzy logicSettore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Artificial IntelligenceFuzzy modelmedicineIn patientMathematicsNasal symptombusiness.industryApproximate reasoningTest (assessment)Data setPhysical therapyArtificial intelligenceDiagnostic decision makingbusinesscomputerNasal symptomsFuzzy Sets and Systems
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