Fuzzy Set Theory

In mathematics, fuzzy sets are sets whose elements have degrees of membership. Fuzzy sets were introduced by Lotfi A. Zadeh and Dieter Klaua 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 used now in different 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.

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. Fuzzy sets generalize classical sets, since the indicator functions of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take 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.

Chapter 1: Fuzzy Set Theory

Chapter 2: Crisp Set Versus Fuzzy Sets

Chapter 3: Operation on Fuzzy Sets

Chapter 4: Fuzzy Numbers and Fuzzy Arithmetic

Chapter 5: Fuzzy Relations and Fuzzy Relations Equations

Chapter 6: Possibility Theory

Chapter 7: Fuzzy Logic

Chapter 8: Uncertainty Based Information

Chapter 9: Fuzzy Decision Making