lthough fuzzy set theory and  sheaf theory have been developed and studied independently,  ulrich hohle shows that a large part of fuzzy set  theory  is in fact a subfield of sheaf theory. many authors have studied mathematical structures, in particular, algebraic structures, in both  categories of these generalized (multi)sets. using hohle's idea, we show that for a (universal) algebra $a$, th...

We consider a digital fuzzy set placed in Oxyplane, with support which is a set of digital points (centroids) in Z. We consider two kinds of convexity of a fuzzy set, namely quasi convexity and strong convexity, and we propose two algorithms for the construction of both kinds of convex hull of a digital fuzzy set.

g e y w o r d s F u z z y numbers, Convexity, Continuity, Convex fuzzy mappings, Linear ordering, Fuzzy Weirstrass theorem, Fuzzy optimization. 1. I N T R O D U C T I O N Let R n denote the n-dimensional Eucl idean space. The suppor t , supp(#) , of a fuzzy set # : R ~ I = [0, 1] is defined as supp(/~) = {x • R n I #(x) > 0}. A fuzzy set # : R n --~ I is called fuzzy convex if #(Ax + (1 A)y) _>...

Abstract In this paper, our aim is to consider the new class of log-convex fuzzy-interval-valued function known as log- s -convex functions (log- fuzzy-IVFs). By concept, we have introduced Hermite–Hadamard inequalities (HH-inequalities) by means fuzzy order relation. This relation defined level-wise through Kulisch–Miranker on interval space. Moreover, some Hermite–Hadamard–Fejér (HH–Fejér-ine...

Cluster Analysis is a fundamental data exploration technique in which there exist a variety of algorithms arising in many different areas of the literature. One of the most popular clustering algorithms is fuzzy c-means which minimises the weighted within-group sum of squares functional. However, this algorithm has a major drawback in that it will only find clusters of the same simple convex sh...