We discuss construction of fuzzy implication and also correlation between negation and implication operators defined on fuzzy values. Two structures for fuzzy implications are studied: the lattice of Step-Ordered Fuzzy Numbers (SOFNs) and the Boolean alg
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This paper analyzes the performance of some fuzzy implications proposed in the bibliography together with the operators needed for their definition and for the fuzzy inference process. Examining the specialized literature, it is clear that the selection of the best fuzzy implication operator has become...
FUZZY algorithmsALGORITHMSIn the present study, a novel parametric family of fuzzy implications is introduced and its properties are examined. The parametric family of implications is produced only via a fuzzy negation. This in turn enables the effortless production of a wide range of implications ...
to fuzzy logic, Mamdani (engineering) implications , some final remarks, and an appendix about properties/laws of type-1 fuzzy sets . 35 examples are used to illustrate this chapter’s important concepts. This is a preview of subscription content,log in via an institutionto check access. ...
TheBestIntervalRepresentationofFuzzyS-Implicationsand Automorphisms Benjam´ınC.Bedregal,RegivanH.N.Santiago,RenataH.S.Reiser,Grac¸alizP.Dimuro Abstract—Theaimofthisworkistoanalyzeintervalfuzzy S-implicationsandintervalautomorphisms.Startingfromany...
This paper presents an analysis of interval-valued S-implications and interval-valued automorphisms, showing a way to obtain an interval-valued S-implication from two S-implications, such that the resulting interval-valued S-implication is said to be obtainable. Some consequences of that are: (1...
Both algorithms are applied to a rainfall-runoff model. Results from the two approaches are compared and the implications of the differences of the results are discussed. The EP is by and large more conservative than the MC method. The results from both approaches show that the output ...
With this in mind, the next fuzzy implications are obtained: IF h1 is 1, THEN μ1 = 1 → u(t) = h1K1[x(t) − Πω(t)] + μ1Γ1ω(t), IF h2 is 1, THEN μ2 = 1 → u(t) = h2K2[x(t) − Πω(t)] + μ2Γ2ω(t). A set of fuzzy rules ...
S. Weber. A general concept of fuzzy connectives, negations and implications based on t -norms and t -conorms. Fuzzy Sets and Systems 11, pp. 115-134, 1983. ArticleMathSciNetMATHGoogle Scholar H.Y. Xu and G. Vukovich. A fuzzy genetic algorithm with effective search and optimization. ...