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66 Fuzzy Logic Theory • 2<br />

rational truth value in the range [0,1], defined by the following equally spaced<br />

partition:<br />

0 1 2 n − 2 n − 1<br />

0 = , , , ..., , = 1.<br />

n − 1 n − 1 n − 1 n −1<br />

n − 1<br />

Each of these truth values describes a degree of truth.<br />

The commonly used n-valued logic, particularly in fuzzy set and fuzzy<br />

systems theories, is the Lukasiewicz-Zadeh n-valued logic. Lukasiewicz<br />

developed an n-valued logic in the 1930s, using only the negation − and the<br />

implication ⇒ logical operations. Based on that, one can define<br />

a ∨ b = ( a ⇒ b ) ⇒ b,<br />

a ∧ b = a ∨ b ,<br />

a ⇔ b = ( a ⇒ b ) ∧ ( b ⇒ a ).<br />

The fuzzy set theory was developed by Zadeh in 1960s, which has been<br />

thoroughly studied in Chapter 1. To develop an n-valued logic, with 2 ≤ n ≤<br />

∞, such that it is isomorphic to the fuzzy set theory in the same way as the<br />

two-valued logic is isomorphic to the classical set theory, Zadeh modified the<br />

Lukasiewicz logic and established an infinite-valued logic, by defining the<br />

following primary logic operations:<br />

a = 1 − a,<br />

a ∧ b = min{ a, b },<br />

a ∨ b = max{ a, b },<br />

a ⇒ b = min{ 1, 1 + b − a },<br />

a ⇔ b = 1 − | a − b |.<br />

It has been shown, in logic theory, that all these logical operations become<br />

the same as those for the two-valued logic when n = 2, and the same as those<br />

for the three-valued logic shown in Table 2.7 when n = 3. More importantly,<br />

when n = ∞, this logic does not restrict the truth values to be rational: they can<br />

be any real numbers in [0,1]. It has also been shown that this infinite-valued<br />

logic is isomorphic to the fuzzy set theory that employs the min, max, and 1−a<br />

operation for fuzzy set intersection, union, and complement, respectively, in<br />

the same way as the classical two-valued logic is isomorphic to the crisp set<br />

theory. These verifications are beyond the scope of this text and, hence, are<br />

omitted.<br />

IV. FUZZY LOGIC AND APPROXIMATE REASONING<br />

Fuzzy logic is a logic; its ultimate goal is to provide foundations for<br />

approximate reasoning using imprecise propositions based on fuzzy set<br />

theory, in a way similar to the classical reasoning using precise propositions<br />

based on the classical set theory.<br />

To introduce this notion, we first recall how the classical reasoning works,<br />

using only precise propositions and the two-valued logic. The following<br />

syllogism is an example of such reasoning in linguistic terms:

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