fuzzy subset - ορισμός. Τι είναι το fuzzy subset
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Τι (ποιος) είναι fuzzy subset - ορισμός

SETS WHOSE ELEMENTS HAVE DEGREES OF MEMBERSHIP
Fuzzy sets; Fuzzy set theory; Fuzzification; Fuzzy subset; Credibility(fuzzy); Fuzzy category; Goguen category; Fuzzy Sets; Fuzzy relation equation; Pythagorean fuzzy set; Degree of membership; Uncertain set
  • Some Key Developments in the Introduction of Fuzzy Set Concepts.<ref name="CADsurvey"/>

fuzzy subset         
In fuzzy logic, a fuzzy subset F of a set S is defined by a "membership function" which gives the degree of membership of each element of S belonging to F.
Cofinal (mathematics)         
IN ORDER THEORY, A SUBSET 𝑌 OF A POSET 𝑋 SUCH THAT FOR ANY ELEMENT OF 𝑋, THERE EXISTS AN ELEMENT OF 𝑌 LARGER THAN IT
Cofinal subset; Cofinal function; Cofinal sequence; Cofinal net; Coinitial; Cofinal set; Final function
In mathematics, a subset B \subseteq A of a preordered set (A, \leq) is said to be cofinal or frequent in A if for every a \in A, it is possible to find an element b in B that is "larger than a" (explicitly, "larger than a" means a \leq b).
Coinitial         
IN ORDER THEORY, A SUBSET 𝑌 OF A POSET 𝑋 SUCH THAT FOR ANY ELEMENT OF 𝑋, THERE EXISTS AN ELEMENT OF 𝑌 LARGER THAN IT
Cofinal subset; Cofinal function; Cofinal sequence; Cofinal net; Coinitial; Cofinal set; Final function
·adj Having a common beginning.

Βικιπαίδεια

Fuzzy set

In mathematics, fuzzy sets (a.k.a. uncertain sets) are sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotfi A. Zadeh in 1965 as an extension of the classical notion of set. At the same time, Salii (1965) defined a more general kind of structure called an L-relation, which he studied in an abstract algebraic context. Fuzzy relations, which are now used throughout fuzzy mathematics and have applications in areas such as linguistics (De Cock, Bodenhofer & Kerre 2000), decision-making (Kuzmin 1982), and clustering (Bezdek 1978), are special cases of L-relations when L is the unit interval [0, 1].

In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition—an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions (aka characteristic functions) of classical sets are special cases of the membership functions of fuzzy sets, if the latter only takes values 0 or 1. In fuzzy set theory, classical bivalent sets are usually called crisp sets. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as bioinformatics.