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In case of fuzzy probabilities, fuzzy logic is applied to propagate them.
In case of fuzzy OR and fuzzy AND, only one of the contributing fuzzy set has an effect on the resultant value.
In case of fuzzy classification, much fewer groups (about six times less) were needed to describe the linear pattern of genetic trend of unknown parents, resulting in a higher precision of the genetic group solutions and explaining the lower prediction error variance of breeding values for this alternative.
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In case of fuzzy- classified genetic groups, Q can essentially contain any value between 0 and 1, depending on the fractions in Q b.
In both cases, the fuzzy sets were isosceles triangles evenly distributed, in the universe of discourses.
Similarly, in case of picture fuzzy set, an extra exponent parameter value is required to be set to obtain best fuzzy cluster sets, thus requiring more computational time [42].
Second, we define a solution for data reduction in the case of fuzzy binary relations.
In the case of fuzzy controllers such a candidate function is not always found, leading to an uncertainty about their stability.
Indeed, in the case of fuzzy positioning the maximum for IPDFT is somewhat shifted to the left, but the distribution is very flat in this region.
The transformed mixed model equations for an animal model with genetic groups derived by Quaas [ 6] are also applicable in the case of fuzzy classification of genetic groups, because the derivation is for any arbitrary matrix Q.
The result of the above theorem can also be simplified as tilde{y}(A + B) = 4*tilde{y}(A){text{ (or) }}tilde{y}(A + B) = 4*tilde{y}(B),where (tilde{y}(A) = tilde{y}(B)) in case of triangular intuitionistic fuzzy number.
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