Rules were presented for assigning coefficients to the inverse of the relationship matrix for fuzzy-classified genetic groups.
The proposed technique in the first type MOQ (Multi Operations Question) uses the matrix concept for fuzzy score.
Such conditions are derived by using Leibniz Newton formula and free weighting matrix method for fuzzy systems under the assumption that sampling time is not greater than some prescribed scalar.
Such conditions are derived by using the Leibniz Newton formula and free-weighting matrix method for fuzzy time-delay systems under the assumption that sampling time is not greater than some prescribed number.
Our main motivation for preparing this paper is to generalize shifted Jacobi function operational matrix for solving fuzzy fractional differential equations of order 0 < v < 1 under Caputo's H-differentiability.
The line graphs can be plotted for fuzzy decision matrix, normalized fuzzy decision matrix and weighted normalized fuzzy decision matrix.
The fuzzification of frequency and severity of the consequences of the incident scenario are described which are basic inputs for fuzzy risk matrix.
Firstly, in terms of the uncertainty of domain mapping information, the fuzzy evaluation matrix for complexity which integrated the weights of experts is constructed based on triangular fuzzy numbers.
Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained.
Specifically, the proposed methods employ fuzzy membership matrix for partitioning initial clusters first.
Post-multiplication of this matrix with the "fuzzy" Q b yields a matrix the rows of which sum to one, for both the distinct and fuzzy classification of genetic groups.
