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| In [[mathematics]]''', Fuzzy sets''' are [[Set (mathematics)|sets]] whose [[Element (mathematics)|elements]] have degrees of membership. Fuzzy sets were introduced by [[Lotfi Asker Zadeh|Lotfi A. Zadeh]]<ref>L. A. Zadeh (1965) [http://www-bisc.cs.berkeley.edu/Zadeh-1965.pdf "Fuzzy sets"]. ''Information and Control'' 8 (3) 338–353.</ref> and Dieter Klaua<ref>Klaua, D. (1965) Über einen Ansatz zur mehrwertigen Mengenlehre. Monatsb. Deutsch. Akad. Wiss. Berlin 7, 859–876. A recent in-depth analysis of this paper has been provided by {{cite doi|10.1016/j.fss.2009.12.005}}</ref> in 1965 as an extension of the classical notion of set.
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| At the same time, Salii (1965) defined a more general kind of structures called ''L''-relations, which were studied by him in an abstract algebraic context. Fuzzy relations, which are used now in different areas, such as [[linguistics]] (De Cock, et al, 2000), [[Decision making|decision-making]] (Kuzmin, 1982) and [[clustering]] (Bezdek, 1978), are special cases of ''L''-relations when ''L'' is the [[unit interval]] [0, 1].
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| In classical [[set theory]], the membership of elements in a set is assessed in binary terms according to a [[Principle of bivalence|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 (mathematics)|membership function]] valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the [[indicator function]]s of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.<ref>D. Dubois and H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York.</ref> In fuzzy set theory, classical bivalent sets are usually called ''[[Crisp set|crisp]]''[[Crisp set|sets]]. The fuzzy set theory
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| can be used in a wide range of domains in which information is incomplete or imprecise,
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| such as [[bioinformatics]].<ref>Lily R. Liang, Shiyong Lu, Xuena Wang, Yi Lu, Vinay Mandal, Dorrelyn Patacsil, and Deepak Kumar, "FM-test: A Fuzzy-Set-Theory-Based Approach to Differential Gene Expression Data Analysis", BMC Bioinformatics, 7 (Suppl 4): S7. 2006.</ref>
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| It has been suggested by Thayer Watkins that Zadeh's ethnicity is an example of a fuzzy set because "His father was Turkish-Iranian (Azerbaijani) and his mother was Russian. His father was a journalist working in Baku, Azerbaijan in the Soviet Union...Lotfi was born in Baku in 1921 and lived there until his family moved to Tehran in 1931."<ref>[http://www.sjsu.edu/faculty/watkins/fuzzysets.htm "Fuzzy Logic: The Logic of Fuzzy Sets"]</ref>
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| ==Definition==
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| A fuzzy set is a pair <math>(U, m)</math> where <math>U</math> is a set and <math>m\colon U \rightarrow [0,1].</math>
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| For each <math>x\in U,</math> the value <math>m(x)</math> is called the '''grade''' of membership of <math>x</math> in <math>(U,m).</math> For a finite set <math>U=\{x_1,\dots,x_n\},</math> the fuzzy set <math>(U, m)</math> is often denoted by <math>\{m(x_1)/x_1,\dots,m(x_n)/x_n\}.</math>
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| Let <math>x \in U.</math> Then <math>x</math> is called '''not included''' in the fuzzy set <math>(U,m)</math> if {{nowrap|<math>m(x) = 0</math>}}, <math>x</math> is called '''fully included''' if {{nowrap|<math>m(x) = 1</math>}}, and <math>x</math> is called a '''fuzzy member''' if {{nowrap|<math>0 < m(x) < 1</math>.<ref>[http://www.aaai.org/aitopics/pmwiki/pmwiki.php/AITopics/FuzzyLogic AAAI]</ref>}}
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| The set <math>\{x\in U\mid m(x)>0\}</math> is called the '''support''' of <math>(U,m)</math> and the set <math>\{x\in U\mid m(x)=1\}</math> is called its '''kernel'''. The function <math>m</math> is called the '''membership function''' of the fuzzy set <math>(U, m).</math>
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| Sometimes, more general variants of the notion of fuzzy set are used, with membership functions taking values in a (fixed or variable) [[algebraic structure|algebra]] or [[structure (mathematical logic)|structure]] <math>L</math> of a given kind; usually it is required that <math>L</math> be at least a [[poset]] or [[lattice (order)|lattice]]. These are usually called '''''L''-fuzzy sets''', to distinguish them from those valued over the unit interval. The usual membership functions with values in [0, 1] are then called [0, 1]-valued membership functions. These kinds of generalizations were first considered in 1967 by [[Joseph Goguen]], who was a student of Zadeh.<ref>[[Joseph Goguen|Goguen, Joseph A.]], 196, "''L''-fuzzy sets". ''Journal of Mathematical Analysis and Applications'' '''18''': 145–174</ref>
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| == Fuzzy logic ==
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| {{main|Fuzzy logic}}
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| As an extension of the case of [[multi-valued logic]], valuations (<math>\mu : \mathit{V}_o \to \mathit{W}</math>) of [[propositional variable]]s (<math>\mathit{V}_o</math>) into a set of membership degrees (<math>\mathit{W}</math>) can be thought of as [[membership function (mathematics)|membership functions]] mapping [[first-order logic|predicates]] into fuzzy sets (or more formally, into an ordered set of fuzzy pairs, called a fuzzy relation). With these valuations, many-valued logic can be extended to allow for fuzzy [[premise]]s from which graded conclusions may be drawn.<ref>[[Siegfried Gottwald]], 2001. ''A Treatise on Many-Valued Logics''. Baldock, Hertfordshire, England: Research Studies Press Ltd., ISBN 978-0-86380-262-1</ref>
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| This extension is sometimes called "fuzzy logic in the narrow sense" as opposed to "fuzzy logic in the wider sense," which originated in the [[engineering]] fields of [[automation|automated]] control and [[knowledge engineering]], and which encompasses many topics involving fuzzy sets and "approximated reasoning."<ref>"The concept of a linguistic variable and its application to approximate reasoning," ''Information Sciences'' '''8''': 199–249, 301–357; '''9''': 43–80.</ref>
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| Industrial applications of fuzzy sets in the context of "fuzzy logic in the wider sense" can be found at [[fuzzy logic]].
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| == Fuzzy number ==
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| {{main|Fuzzy number}}
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| A '''fuzzy number''' is a [[Convex set|convex]], [[normalizing constant|normalized]] fuzzy set <math>\tilde{\mathit{A}}\subseteq\mathbb{R}</math>
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| whose membership function is at least segmentally [[continuous function|continuous]] and has the functional value <math>\mu_{A}(x)=1</math> at precisely one element.
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| This can be likened to the [[funfair]] game "guess your weight," where someone guesses the contestant's weight, with closer guesses being more correct, and where the guesser "wins" if he or she guesses near enough to the contestant's weight, with the actual weight being completely correct (mapping to 1 by the membership function).
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| == Fuzzy interval ==
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| A '''fuzzy interval''' is an uncertain set <math>\tilde{\mathit{A}}\subseteq\mathbb{R}</math> with a mean interval whose elements possess the membership function value <math>\mu_{A}(x)=1</math>. As in fuzzy numbers, the membership function must be [[Convex set|convex]], [[Normalizing constant|normalized]], at least segmentally [[continuous function|continuous]].<ref>"Fuzzy sets as a basis for a theory of possibility," ''Fuzzy Sets and Systems'' '''1''': 3–28</ref>
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| == Fuzzy relation equation ==
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| The [[fuzzy relation equation]] is an equation of the form A · R = B, where A and B are fuzzy sets, R is a fuzzy relation, and A · R stands for the composition of A with R.
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| ==Axiomatic definition of credibility==
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| <ref>Liu, Baoding. "Uncertain theory: an introduction to its axiomatic foundations." Berlin: Springer-Verlag (2004).</ref> Let A be a non-empty set and P(A) be the power set of A . The set function is known as credibility measure if it satisfies following condition
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| * Axiom 1: <math> Cr \lbrace A \rbrace = 1 </math>
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| * Axiom 2: If B is subset of C, then, <math> Cr \lbrace B \rbrace \leq Cr \lbrace C \rbrace </math>
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| * Axiom 3: <math>Cr \lbrace B \rbrace + Cr \lbrace B^{c} \rbrace = 1</math>
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| * Axiom 4: <math> Cr \lbrace \cup A_{i} \rbrace = sup_{i}(A_{i}) </math> , for any event <math>A_{i}</math> with <math> sup_{i} Cr \lbrace A_{i} \rbrace < 0.5</math>
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| Cr{B} indicates how frequently event B will occur.
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| ==Credibility inversion theorem==
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| <ref>Liu, Baoding, and Yian-Kui Liu. "Expected value of fuzzy variable and fuzzy expected value models." Fuzzy Systems, IEEE Transactions on 10.4 (2002): 445-450.</ref> Let A be a fuzzy variable with membership function u. Then for any set B of real numbers, we have
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| :<math> Cr\lbrace A \in B \rbrace = \dfrac{1}{2}(Sup_{t \in B} u(t) + 1 - Sup_{t \in B^{c}} u(t)) </math>
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| ==Expected Value==
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| <ref>Liu, Baoding, and Yian-Kui Liu. "Expected value of fuzzy variable and fuzzy expected value models." Fuzzy Systems, IEEE Transactions on 10.4 (2002): 445-450.</ref> Let A be a fuzzy variable. Then the expected value is
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| :<math> E[A] = \int_0^\infty Cr \lbrace A \geq t \rbrace \,dt - \int_{-\infty}^0 Cr \lbrace A \leq t \rbrace \,dt.</math>
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| ==Entropy==
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| <ref>Xuecheng, Liu. "Entropy, distance measure and similarity measure of fuzzy sets and their relations." Fuzzy sets and systems 52.3 (1992): 305-318.</ref> Let A be a fuzzy variable with a continuous membership function. Then its entropy is
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| :<math> H[A] = \int_{- \infty}^\infty S(Cr \lbrace A \geq t \rbrace )\,dt.</math>
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|
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| Where
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| :<math> S(y) = -ylny - (1 - y )ln(1-y)</math>
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| ==Generalizations==
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| There are many mathematical constructions similar to or more general than fuzzy sets. Since fuzzy sets were introduced in 1965, a lot of new mathematical constructions and theories treating imprecision, inexactness, ambiguity, and uncertainty have been developed. Some of these constructions and theories are extensions of fuzzy set theory, while others try to mathematically model imprecision and uncertainty in a different way (Burgin and Chunihin, 1997; Kerre, 2001; Deschrijver and Kerre, 2003).
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| The diversity of such constructions and corresponding theories includes:
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| - interval sets (Moore, 1966),
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| - ''L''-fuzzy sets (Goguen, 1967),
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| - flou sets (Gentilhomme, 1968),
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| - Boolean-valued fuzzy sets (Brown, 1971),
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| - type-2 fuzzy sets and type-n fuzzy sets (Zadeh, 1975),
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| - set-valued sets (Chapin, 1974; 1975),
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| - interval-valued fuzzy sets (Grattan-Guiness, 1975; Jahn, 1975; Sambuc, 1975; Zadeh, 1975),
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| - functions as generalizations of fuzzy sets and multisets (Lake, 1976),
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| - level fuzzy sets (Radecki, 1977)
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| - underdetermined sets (Narinyani, 1980),
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| - rough sets (Pawlak, 1982),
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| - intuitionistic fuzzy sets (Atanassov, 1983),
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| - fuzzy multisets (Yager, 1986),
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| - intuitionistic ''L''-fuzzy sets (Atanassov, 1986),
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| - rough multisets (Grzymala-Busse, 1987),
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| - fuzzy rough sets (Nakamura, 1988),
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| - real-valued fuzzy sets (Blizard, 1989),
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| - vague sets (Wen-Lung Gau and Buehrer, 1993),
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| - Q-sets (Gylys, 1994)
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| - shadowed sets (Pedrycz, 1998),
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| - α-level sets (Yao, 1997),
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| - genuine sets (Demirci, 1999),
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| - neutrosophic sets (Smarandache, 1999),
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| - [[soft set]]s (Molodtsov, 1999),
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| - intuitionistic fuzzy rough sets (Cornelis, De Cock and Kerre, 2003)
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| - blurry sets (Smith, 2004)
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| - ''L''-fuzzy rough sets (Radzikowska and Kerre, 2004),
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| - generalized rough fuzzy sets (Feng, 2010)
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| - rough intuitionistic fuzzy sets (Thomas and Nair, 2011),
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| - soft rough fuzzy sets (Meng, Zhang and Qin, 2011)
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| - soft fuzzy rough sets (Meng, Zhang and Qin, 2011)
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| - soft multisets (Alkhazaleh, Salleh and Hassan, 2011)
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| - fuzzy soft multisets (Alkhazaleh and Salleh, 2012)
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| ==See also==
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| <div style="-moz-column-count:2; column-count:2;"> | |
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| * [[Alternative set theory]]
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| * [[Defuzzification]]
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| * [[Fuzzy concept]]
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| * [[Fuzzy mathematics]]
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| * [[Fuzzy measure theory]]
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| * [[Fuzzy set operations]]
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| * [[Fuzzy subalgebra]]
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| * [[Linear partial information]]
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| * [[Neuro-fuzzy]]
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| * [[Rough fuzzy hybridization]]
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| * [[Rough set]]
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| * [[Sørensen similarity index]]
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| * [[Type-2 Fuzzy Sets and Systems]]
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| * [[Uncertainty]]
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| * [[Interval finite element]]
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| * [[Multiset]]
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| </div> | |
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| ==References==
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| {{reflist}}
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| == Further reading ==
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| * Alkhazaleh, S. and Salleh, A.R. Fuzzy Soft Multiset Theory, Abstract and Applied Analysis, 2012, article ID 350600, 20 p.
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| * Alkhazaleh, S., Salleh, A.R. and Hassan, N. Soft Multisets Theory, Applied Mathematical Sciences, v. 5, No. 72, 2011, pp. 3561–3573
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| * Atanassov, K. T. (1983) Intuitionistic fuzzy sets, VII ITKR's Session, Sofia (deposited in Central Sci.-Technical Library of Bulg. Acad. of Sci., 1697/84) (in Bulgarian)
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| * Atanasov, K. (1986) Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, v. 20, No. 1, pp. 87–96
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| * Bezdek, J.C. (1978) Fuzzy partitions and relations and axiomatic basis for clustering, Fuzzy Sets and Systems, v.1, pp. 111–127
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| * Blizard, W.D. (1989) Real-valued Multisets and Fuzzy Sets, Fuzzy Sets and Systems, v. 33, pp. 77–97
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| * Brown, J.G. (1971) A Note on Fuzzy Sets, Information and Control, v. 18, pp. 32–39
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| * Chapin, E.W. (1974) Set-valued Set Theory, I, Notre Dame J. Formal Logic, v. 15, pp. 619–634
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| * Chapin, E.W. (1975) Set-valued Set Theory, II, Notre Dame J. Formal Logic, v. 16, pp. 255–267
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| * Chris Cornelis, Martine De Cock and Etienne E. Kerre, Intuitionistic fuzzy rough sets: at the crossroads of imperfect knowledge, Expert Systems, v. 20, issue 5, pp. 260–270, 2003
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| * Cornelis, C., Deschrijver, C., and Kerre, E. E. (2004) Implication in intuitionistic and interval-valued fuzzy set theory: construction, classification, application, International Journal of Approximate Reasoning , v. 35, pp. 55–95
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| * Martine De Cock, Ulrich Bodenhofer, and Etienne E. Kerre, Modelling Linguistic Expressions Using Fuzzy Relations, (2000) Proceedings 6th International Conference on Soft Computing. Iizuka 2000, Iizuka, Japan (1-4 october 2000) CDROM. p. 353-360
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| * Demirci, M. (1999) Genuine Sets, Fuzzy Sets and Systems, v. 105, pp. 377–384
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| * Deschrijver, G. and Kerre, E.E. On the relationship between some extensions of fuzzy set theory, Fuzzy Sets and Systems, v. 133, no. 2, pp. 227–235, 2003
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| * {{cite book|editor=Didier Dubois, Henri M. Prade|title=Fundamentals of fuzzy sets|year=2000|publisher=Springer|isbn=978-0-7923-7732-0|series=The Handbooks of Fuzzy Sets Series|volume=7}}
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| * Feng F. Generalized Rough Fuzzy Sets Based on Soft Sets, Soft Computing, July 2010, Volume 14, Issue 9, pp 899–911
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| * Gentilhomme, Y. (1968) Les ensembles flous en linguistique, Cahiers Linguistique Theoretique Appliqee, 5, pp. 47–63
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| * Gogen, J.A. (1967) L-fuzzy Sets, Journal Math. Analysis Appl., v. 18, pp. 145–174
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| * {{cite doi|10.1007/s11225-006-7197-8}}. {{cite doi|10.1007/s11225-006-9001-1}}.
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| * Grattan-Guiness, I. (1975) Fuzzy membership mapped onto interval and many-valued quantities. Z. Math. Logik. Grundladen Math. 22, pp. 149–160.
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| * Grzymala-Busse, J. Learning from examples based on rough multisets, in Proceedings of the 2nd International Symposium on Methodologies for Intelligent Systems, Charlotte, NC, USA, 1987, pp. 325–332
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| * Gylys, R. P. (1994) Quantal sets and sheaves over quantales, Liet. Matem. Rink. , v. 34, No. 1, pp. 9–31.
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| * {{cite book|editor=Ulrich Höhle, Stephen Ernest Rodabaugh|title=Mathematics of fuzzy sets: logic, topology, and measure theory|year=1999|publisher=Springer|isbn=978-0-7923-8388-8|series=The Handbooks of Fuzzy Sets Series|volume=3}}
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| * Jahn, K. U. (1975) Intervall-wertige Mengen, Math.Nach. 68, pp. 115–132
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| * Kerre, E.E. A first view on the alternatives of fuzzy set theory, Computational Intelligence in Theory and Practice (B. Reusch, K-H . Temme, eds) Physica-Verlag, Heidelberg (ISBN 3-7908-1357-5), 2001, pp. 55– 72
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| * {{cite book|author1=George J. Klir|author2=Bo Yuan|title=Fuzzy sets and fuzzy logic: theory and applications|year=1995|publisher=Prentice Hall|isbn=978-0-13-101171-7}}
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| * Kuzmin,V.B. Building Group Decisions in Spaces of Strict and Fuzzy Binary Relations, Nauka, Moscow, 1982 (in Russian)
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| * Lake, J. (1976) Sets, fuzzy sets, multisets and functions, J. London Math. Soc., II Ser., v. 12, pp. 323–326
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| * Meng, D., Zhang, X. and Qin, K. Soft rough fuzzy sets and soft fuzzy rough sets, ‘Computers & Mathematics with Applications’, v. 62, issue 12, 2011, pp. 4635–4645
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| * Miyamoto, S. Fuzzy Multisets and their Generalizations, in ‘Multiset Processing’, LNCS 2235, pp. 225–235, 2001
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| * Molodtsov, O. (1999) Soft set theory – first results, Computers & Mathematics with Applications, v. 37, No. 4/5, pp. 19–31
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| * Moore, R.E. Interval Analysis, New York, Prentice-Hall, 1966
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| * Nakamura, A. (1988) Fuzzy rough sets, ‘Notes on Multiple-valued Logic in Japan’, v. 9, pp. 1–8
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| * Narinyani, A.S. Underdetermined Sets – A new datatype for knowledge representation, Preprint 232, Project VOSTOK, issue 4, Novosibirsk, Computing Center, USSR Academy of Sciences, 1980
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| * Pedrycz, W. Shadowed sets: representing and processing fuzzy sets, IEEE Transactions on System, Man, and Cybernetics, Part B, 28, 103-109, 1998.
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| * Radecki, T. Level Fuzzy Sets, ‘Journal of Cybernetics’, Volume 7, Issue 3-4, 1977
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| * Radzikowska, A.M. and Etienne E. Kerre, E.E. On L-Fuzzy Rough Sets, Artificial Intelligence and Soft Computing - ICAISC 2004, 7th International Conference, Zakopane, Poland, June 7–11, 2004, Proceedings; 01/2004
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| * Salii, V.N. (1965) Binary L-relations, Izv. Vysh. Uchebn. Zaved., Matematika, v. 44, No.1, pp. 133–145 (in Russian)
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| * Sambuc, R. Fonctions φ-floues: Application a l'aide au diagnostic en pathologie thyroidienne, Ph. D. Thesis Univ. Marseille, France, 1975.
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| * Seising, Rudolf: The Fuzzification of Systems. The Genesis of Fuzzy Set Theory and Its Initial Applications—Developments up to the 1970s (Studies in Fuzziness and Soft Computing, Vol. 216) Berlin, New York, [et al.]: Springer 2007.
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| * Smarandache, F. (2002) A unifying field in logics: neutrosophic logic, Multiple Valued Logic, v. 8, No. 3, pp. 385–438
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| * Smith, N.J.J. (2004) Vagueness and blurry sets, ‘J. of Phil. Logic’, 33, pp. 165–235
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| * Thomas, K.V. and L. S. Nair, Rough intuitionistic fuzzy sets in a lattice, ‘International Mathematical Forum’, Vol. 6, 2011, no. 27, 1327 - 1335
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| * Yager, R. R. (1986) On the Theory of Bags, International Journal of General Systems, v. 13, pp. 23–37
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| * Yao, Y.Y., Combination of rough and fuzzy sets based on α-level sets, in: Rough Sets and Data Mining: Analysis for Imprecise Data, Lin, T.Y. and Cercone, N. (Eds.), Kluwer Academic Publishers, Boston, pp. 301–321, 1997.
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| * Y. Y. Yao, A comparative study of fuzzy sets and rough sets, Information Sciences, v. 109, Issue 1-4, 1998, pp. 227 – 242
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| * Zadeh, L. (1975) The concept of a linguistic variable and its application to approximate reasoning–I, Inform. Sci., v. 8, pp. 199–249
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| * {{cite book|author=Hans-Jürgen Zimmermann|title=Fuzzy set theory—and its applications|year=2001|publisher=Kluwer|isbn=978-0-7923-7435-0|edition=4th}}
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| ==External links==
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| * [http://www.uncertainty-in-engineering.net/uncertainty_models/fuzziness Uncertainty model Fuzziness]
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| * [http://www.elsevier.com/wps/find/journaldescription.cws_home/505545/description#description Fuzzy Systems Journal]
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| * [http://www.scholarpedia.org/article/Fuzzy_sets ScholarPedia]
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| * [http://www.uncertainty-in-engineering.net/uncertainty_methods/fuzzy_analysis/ The Algorithm of Fuzzy Analysis]
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| * [http://tizhoosh.uwaterloo.ca/Research/fuzzy_image_processing.htm Fuzzy Image Processing]
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| {{logic}}
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| {{Set theory}}
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| {{DEFAULTSORT:Fuzzy Set}}
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| [[Category:Fuzzy logic]]
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| [[Category:Systems of set theory]]
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