Fuzzy subset

To be Completed !! Template:TOC-right

Introduction

The term fuzzy subset is a generalization of the subset concept from set theory. Observe that we can obtain a subset of a given set S by considering the extension of a well defined property P in S. Indeed, the axiom of comprehension reads that a subset B of S exists whose members are precisely those objects in S satisfying P. For example if S is the set of natural numbers and P is the property "to be prime", then the subset B of prime numbers is defined. Assume that P is a vague property as "to be big", "to be young": is there a way to define the extension of P ? For example:

is there a precise definition of the notion of set of big numbers ?

The notion of fuzzy subset

An attempt to give an answer to such a question was proposed in 1965 by Lotfi Zadeh and at the same time, by Dieter Klaua in the framework of multi-valued logic. Now recall that the characteristic function of a classical subset X of S is the map c_X : → {0,1} such that c_X(x) = 1 if x is an element in X and c_X(x) = 0 otherwise. Obviously, it is possible to identify every subset X with its characteristic function c_X and therefore the extension of a property with a suitable characteristic function. This suggests that we can define the subset of big elements by a generalized characteristic function in which instead of the Boolean algebra {0,1} we can consider, for example, the interval [0,1]. The following is a precise definition.

Definition. Let S be a nonempty set, then an L-subset or fuzzy subset of S is a map s from S into [0,1]. We denote by [0,1]^S the class of all the fuzzy subsets of S. If S₁,...S_n are nonempty sets then a fuzzy subset of S₁×. . .×S_n is called an n-ary L-relation.

The elements in [0,1] are interpreted as truth values and, in accordance, for every x in S, the number s(x) is interpreted as the membership degree of x to s. We say that a fuzzy subset s is crisp if s(x) is in {0,1} for every x in S. By associating every classical subsets of S with its characteristic function, we can identify the subsets of S with the crisp fuzzy subsets. In particular we call "empty subset" of S the fuzzy subset of S constantly equal to 0. Notice that in such a way there is not a unique empty subsets.

Some set-theoretical notions for fuzzy subsets

In classical mathematics the definitions of union, intersection and complement are related with the interpretation of the basic logical connectives $\vee ,\wedge ,\neg$. In order to define the same operations for fuzzy subsets, we have to fix suitable operations $\oplus ,\otimes$ and ~ in [0,1] to interpret these connectives. Once this was done, we can set

$(s\cup t)(x)=s(x)\oplus t(x)$,

$(s\cap t)(x)=s(x)\otimes t(x)$,

$(-s)(x)=~s(x)$.

In such a way an algebraic structure $([0,1]^{S},\cup ,\cap ,-,\emptyset ,S)$ is defined and this structure is the direct power of the structure $([0,1],\oplus ,\otimes ,$ ~,0 ,1) with index set S. In Zadeh's original papers the operations $\oplus ,\otimes$, ~ are defined by setting for every x and y in [0,1]:

$x\otimes y$ = min(x, y) ; $x\oplus y$ = max(x,y) ; $~x$ = 1-x.

In such a case $([0,1]^{F},\cup ,\cap ,-,\emptyset ,S)$ is a complete, completely distributive lattice with an involution. Several authors prefer to consider different operations, as an example to assume that $\otimes$ is a triangular norm in [0,1] and that $\oplus$ is the corresponding triangular co-norm.

In all the cases the interpretation of a logical connective is conservative in the sense that its restriction to {0,1} coincides with the classical one. This entails that the map associating any subset X of a set S with the related characteristic function is an embedding of the Boolean algebra $(\{0,1\}^{S},\cup ,\cap ,-,\emptyset ,S)$ into the algebra $(L^{S},\cup ,\cap ,-,\emptyset ,S)$.

Valuation structures

In the framework of fuzzy logic usually one admits sets of truth values different from [0,1]. Usually one considers bounded lattices equipped with suitable operations to interpret the logical connectives.

Truth degree and belief degree: fuzzy logic and probability

Many peoples compare fuzzy logic with probability theory since both refer to the interval [0,1]. However, they are conceptually distinct since we have not confuse a degree of truth with a probability measure. To illustrate the difference, consider the following example: Let α be the claim "the rose on the table is red" and imagine we can freely examine such a rose (complete information) but, as a matter of fact, the color looks not exactly red. Then α is neither fully true nor fully false and we can express that by assigning to α a truth degree, as an example 0.8, different from 0 and 1 (fuzziness). This truth degree does not depend on the information we have since it is assigned in a situation of complete information. Now, imagine a world in which all the roses are either clearly red or clearly yellow. In such a world α is either true or false but, inasmuch as we cannot examine the rose on the table, we are not able to know what is the case. Nevertheless, we have an opinion about the possible color of that rose and we could assign to α a number, as an example 0.8, as a subjective measure of our degree of belief in α (probability). In such a case this number depends on the information we have and, for example, it can vary if we have some new information on the taste of the possessor of the rose.

See also

Fuzzy logic Fuzzy control system Neuro-fuzzy Fuzzy subalgebra Fuzzy associative matrix FuzzyCLIPS expert system Paradox of the heap Pattern recognition Rough set

Bibliography

• Cox E., The Fuzzy Systems Handbook (1994), ISBN 0-12-194270-8
• Elkan C.. The Paradoxical Success of Fuzzy Logic. November 1993. Available from Elkan's home page.
• Gerla G., Fuzzy logic: Mathematical Tools for Approximate Reasoning, Kluwer, 2001.
• Gottwald S., A treatase on Multi-Valued Logics, Research Studies Press LTD, Baldock 2001.
• Hájek P., Metamathematics of fuzzy logic. Kluwer 1998.
• Höppner F., Klawonn F., Kruse R. and Runkler T., Fuzzy Cluster Analysis (1999), ISBN 0-471-98864-2.
• Klaua D., Über einen Ansatz zur mehrwertigen Mengenlehre, Monatsberichte der Deutschen Akademie der Wissenschaften Berlin, vol 7 (1965), pp 859-867.
• Klir G. and Folger T., Fuzzy Sets, Uncertainty, and Information (1988), ISBN 0-13-345984-5.
• Klir G. , UTE H. St. Clair and Bo Yuan Fuzzy Set Theory Foundations and Applications,1997.
• Klir G. and Bo Yuan, Fuzzy Sets and Fuzzy Logic (1995) ISBN 0-13-101171-5
• Kosko B., Fuzzy Thinking: The New Science of Fuzzy Logic (1993), Hyperion. ISBN 0-7868-8021-X
• Novák V., Perfilieva I, Mockor J., Mathematical Principles of Fuzzy Logic, Kluwer Academic Publishers, Dordrecht, (1999).
• Yager R. and Filev D., Essentials of Fuzzy Modeling and Control (1994), ISBN 0-471-01761-2
• Zimmermann H., Fuzzy Set Theory and its Applications (2001), ISBN 0-7923-7435-5.
• Zadeh L.A., Fuzzy Sets, Information and Control, 8 (1965) 338-353.