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| By the expression '''Fuzzy logic''' one denotes several topics which are related with the notion of [[fuzzy subset]] defined in [[1965]] by [[Lotfi Asker Zadeh|Lotfi Zadeh]] at the [[University of California, Berkeley]]. Given a nonempty set ''S'', a ''fuzzy subset'' of ''S'' is a map ''s'' from ''S'' into the interval [0,1]. Then an element in [0,1] is 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''. In other words, a fuzzy subset is a characteristic function in which graded truth values are admitted.
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| Such a notion enables us to represent the extension of predicates and relations as "big","slow", "near" "similar", which are vague in nature.
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| Observe that there are two possible interpretations of the word "fuzzy logic". The first one is related with an informal utilization of the notion of fuzzy set and it is devoted to the applications. In such a case should be better expressions as "[[fuzzy set theory]]" or "fuzzy logic in board sense". Another interpretation is given in considering fuzzy logic as a chapter of formal logic. In such a case one uses the expression "fuzzy logic in narrow sense" or [[formal fuzzy logic]].
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| == Fuzzy logic and probability ==
| | '''Fuzzy control''' is the main success of fuzzy set theory and it is devoted to useful applications. |
| | | The idea is that we can consider IF-THEN rules in which fuzzy quantities are involved. |
| 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:
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| Let <math>\alpha</math> 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 <math>\alpha</math> is neither fully true nor fully false and we can express that by assigning to <math>\alpha</math> 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.
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| Now, imagine a world in which all the roses are either clearly red or clearly yellow. In such a world <math>\alpha</math> 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 <math>\alpha</math> a number, as an example 0.8, as a subjective measure of our degree of belief in <math>\alpha</math> (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.
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| == Some set-theoretical notion for fuzzy subsets ==
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| In classical mathematics the definitions of union, intersection and complement are related with the interpretation of the basic logical connectives <math>\vee, \wedge, \neg</math>. Likewise, the corresponding operations for fuzzy subsets are related with the interpretation of these connectives in a multivalued logic, i.e. with the corresponding operations <math> \oplus, \otimes</math>, '''-'''. So the union, intersection and complement are defined by setting
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| <math>s\cup t(x) = s(x)\vee t(x)<math>, <math>s\cap t(x) = s(x)\wedge t(x)<math>.
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| In Zadeh's original papers the [[logical connectives]] are usually interpreted by these operations are defined by setting for every ''x'' and ''y'' in [0,1]:
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| <math> x\otimes y </math> = minimum(''x'', ''y'')
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| <math> x\oplus y </math> = maximum(''x'',''y'')
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| '''-''' <math>(x) </math> = 1 - ''x''.
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| In accordance, if ''s'' and ''t'' are two fuzzy subset
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| === Further interpretations of the connectives ===
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| Zadeh's definitions of the connectives are not the only possible. We list the main definitions.
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| * [[Basic propositional fuzzy logic]] BL is an axiomatization of logic where [[conjunction]] is defined by a continuous [[triangular norm|t-norm]], and implication is defined as the residuum of the t-norm. Its [[structure (mathematical logic)|model]]s correspond to [[BL-algebra]]s.
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| * [[Lukasiewicz fuzzy logic|Łukasiewicz fuzzy logic]] is a special case of basic fuzzy logic where conjunction is Łukasiewicz t-norm. It has the axioms of basic logic plus an axiom of double negation (so it is not [[intuitionistic logic]]), and its models correspond to [[MV-algebra]]s.
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| * [[Godel fuzzy logic|Gödel fuzzy logic]] is a special case of basic fuzzy logic where conjunction is [[Gödel]] t-norm. It has the axioms of basic logic plus an axiom of idempotence of conjunction, and its models are called [[G-algebra]]s.
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| * [[Product fuzzy logic]] is a special case of basic fuzzy logic where conjunction is product t-norm. It has the axioms of basic logic plus another axiom, and its models are called [[product algebra]]s.
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| * [[Monoidal t-norm logic]] MTL is a generalization of basic fuzzy logic BL where conjunction is realized by a ''left''-continuous t-norm. Its models (MTL-algebras) are prelinear commutative bounded integral [[residuated lattice]]s.
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| * [[Rational Pavelka logic]] is a generalization of [[multi-valued logic]]. It is an extension of Łukasziewicz fuzzy logic with additional constants.
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| All these logics encompass the traditional [[propositional logic]] (whose models correspond to [[Boolean algebra]]s).
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| An extension of such a theory to the general case of the L-subsets is proposed in a paper by G. Gerla. In such a paper one refer to the theory of effective domains.
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| == See also == | | == See also == |
| * [[Neuro-fuzzy]]
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| * [[Fuzzy subalgebra]] | | * [[Fuzzy subalgebra]] |
| * [[Fuzzy associative matrix]] | | * [[Fuzzy associative matrix]] |
| * [[FuzzyCLIPS]] expert system | | * [[FuzzyCLIPS]] expert system |
| * [[Fuzzy control system]] | | * [[Fuzzy control]] |
| * [[Fuzzy set]] | | * [[Fuzzy set]] |
| | * [[Multi-valued logic]] |
| | * [[Neuro-fuzzy]] |
| * [[Paradox of the heap]] | | * [[Paradox of the heap]] |
| * [[Pattern recognition]] | | * [[Pattern recognition]] |
| * [[Rough set]] | | * [[Rough set]] |
| | | * [[Soft-computing]] |
| == Bibliography ==
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| * Chang C. C.,Keisler H. J., ''Continuous Model Theory'', Princeton University Press, Princeton, 1996.
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| * Cignoli R., D’Ottaviano I. M. L. , Mundici D. , ‘’Algebraic Foundations of Many-Valued Reasoning’’. Kluwer, Dordrecht, 1999.
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| * Cox E., ''The Fuzzy Systems Handbook'' (1994), ISBN 0-12-194270-8
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| * Elkan C.. ''The Paradoxical Success of Fuzzy Logic''. November 1993. Available from [http://www.cse.ucsd.edu/users/elkan/ Elkan's home page].
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| * Hájek P., ''Metamathematics of fuzzy logic''. Kluwer 1998.
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| * Hájek P., Fuzzy logic and arithmetical hierarchy, ''Fuzzy Sets and Systems'', 3, (1995), 359-363.
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| * Höppner F., Klawonn F., Kruse R. and Runkler T., ''Fuzzy Cluster Analysis'' (1999), ISBN 0-471-98864-2.
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| * Klir G. and Folger T., ''Fuzzy Sets, Uncertainty, and Information'' (1988), ISBN 0-13-345984-5.
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| * Klir G. , UTE H. St. Clair and Bo Yuan ''Fuzzy Set Theory Foundations and Applications'',1997.
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| * Klir G. and Bo Yuan, ''Fuzzy Sets and Fuzzy Logic'' (1995) ISBN 0-13-101171-5
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| * [[Bart Kosko]], ''Fuzzy Thinking: The New Science of Fuzzy Logic'' (1993), Hyperion. ISBN 0-7868-8021-X | |
| * Montagna F., Three complexity problems in quantified fuzzy logic. ''Studia Logica'', 68,(2001), 143-152.
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| * Novák V., Perfilieva I, Mockor J., Mathematical Principles of Fuzzy Logic, Kluwer Academic Publishers, Dordrecht, (1999).
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| * Yager R. and Filev D., ''Essentials of Fuzzy Modeling and Control'' (1994), ISBN 0-471-01761-2
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| * Zimmermann H., ''Fuzzy Set Theory and its Applications'' (2001), ISBN 0-7923-7435-5.
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| * Kevin M. Passino and Stephen Yurkovich, ''Fuzzy Control'', Addison Wesley Longman, Menlo Park, CA, 1998.
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| * Wiedermann J. , Characterizing the super-Turing computing power and efficiency of classical fuzzy Turing machines, ''Theor. Comput. Sci.'' 317, (2004), 61-69.
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| * Zadeh L.A., Fuzzy algorithms, ''Information and Control'', 5,(1968), 94-102.
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| * Zadeh L.A., Fuzzy Sets, ‘’Information and Control’’, 8 (1965) 338353.
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| * Zemankova-Leech, M., ''Fuzzy Relational Data Bases'' (1983), Ph. D. Dissertation, Florida State University.
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