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This study is connected with new Generalized Maximum Fuzzy Entropy Methods (GMax(F)EntM) in the form of MinMax(F)EntM and MaxMax(F)EntM belonging to us. These methods are based on primary maximizing Max(F)Ent measure for fixed moment vector function in order to obtain the special functional with maximum values of Max(F)Ent measure and secondary optimization of mentioned functional with respect to moment vector functions. Distributions, in other words sets of successive values of estimated membership function closest to (furthest from) the given membership function in the sense of Max(F)Ent measure, obtained by mentioned methods are defined as (MinMax(F)Ent )m which is closest to a given membership function and (MaxMax(F)Ent )m which is furthest from a given membership function. The aim of this study consists of applying MinMax(F)EntM and MaxMax(F)EntM on given wind speed data. Obtained results are realized by using MATLAB programme. The performances of distributions (MinMax(F)Ent)m and (MaxMax(F)Ent )m generated by using Generalized Maximum Fuzzy Entropy Methods are established by Chi-Square, Root Mean Square Error criterias and Max(F)Ent measure.

journal of mathematics and system science

Generalized Maximum Fuzzy Entropy Methods with Applications on Wind Speed Data rity in the Case, When Kernel Contain Logarithmic Singularity and its Power