Fuzzy subset

The notion of fuzzy subset

Given a subset X of a set S its characteristic function is the map $c_{X}:S\rightarrow \{0,1\}$ such that $c_{X}(x)=1$ if x is an element in X and $c_{X}(x)=0$ otherwise. The notion of fuzzy subset is obtained by substituting the Boolean algebra {0,1} with the complete lattice [0,1]. In other words, a fuzzy subset is a characteristic function in which intermediate truth values are admitted. The following is a precise definition.

Definition Given a nonempty set S, a fuzzy subset of S is a map s from S into the interval [0,1]. We say that s is crisp if $s(x)\in \{0,1\}$ for every $x\in S$.

The idea is that such a notion enables us to represent the extension of predicates as "big","slow", "near" "similar", which are vague in nature. Indeed, the elements in [0,1] are interpreted as truth values and, in accordance, for every x in S, the value s(x) is interpreted as the membership degree of x to s. By associating every classical subsets of S with its caracteristic function, we can identify the subsets of S with the crisp fuzzy subsets. In particular we identify $\emptyset$ with the fuzzy subset constantly equal to 0 and $S$ with the fuzzy subset constantly equal to 1.

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 a multi-valued logic and therefore suitable operations $\oplus ,\otimes$ and ~ to interpret these connectives. In such a case, se 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, if we denote by $F(S)$ the class of all the fuzzy subsets of S, one defines an algebraic structure $(F(S),\cup ,\cap ,-)$. Such a structure is the direct power of the structure $([0,1],\oplus ,\otimes ,$~) with index set S. Also, an inclusion relation is defined by setting

$s\subseteq t\Leftrightarrow s(x)\leq t(x)$ for every $x\in S$.

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 the structure $(F(S),\cup ,\cap ,-)$ 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 and that $\oplus$ is the corresponding triangular co-norm.

An extension of these definitions to the general case in which instead of [0,1] we consider different algebraic structures is obvious.

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 $\alpha$ be the claim "the rose on the table is red" and imagine we can freely examine the rose (complete knowledge) but, as a matter of fact, the color looks not exactly red. Then $\alpha$ is neither fully true nor fully false and we can express that by assigning to $\alpha$ a truth value, as an example 0.8, different from 0 and 1 (fuzziness). This truth value does not depend on the information we have since this information is complete.

Now, imagine a world in which all the roses are either clearly red or clearly yellow. In such a world $\alpha$ 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 $\alpha$ a number, as an example 0.8, as a subjective measure of our degree of belief in $\alpha$ (probability). In such a case this number depends strongly from 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

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

Bibliography

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