Fuzzy set

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In mathematics, fuzzy sets are sets whose elements have degrees of membership. Fuzzy sets were introduced by Lotfi A. Zadeh[1] and Dieter Klaua[2] in 1965 as an extension of the classical notion of set. 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 (Kuzmin, 1982) and clustering (Bezdek, 1978), are special cases of L-relations when L is the unit interval [0, 1].

In classical set theory, the membership of elements in a set is assessed in binary terms according to a 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 valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.[3] In fuzzy set theory, classical bivalent sets are usually called crisp sets. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as bioinformatics.[4]

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."[5]


A fuzzy set is a pair where is a set and

For each the value is called the grade of membership of in For a finite set the fuzzy set is often denoted by

Let Then is called not included in the fuzzy set if , is called fully included if , and is called a fuzzy member if .[6] The set is called the support of and the set is called its kernel or core. The function is called the membership function of the fuzzy set

Sometimes, more general variants of the notion of fuzzy set are used, with membership functions taking values in a (fixed or variable) algebra or structure of a given kind; usually it is required that be at least a poset or 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.[7]

Fuzzy logic


As an extension of the case of multi-valued logic, valuations () of propositional variables () into a set of membership degrees () can be thought of as membership functions mapping 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 premises from which graded conclusions may be drawn.[8]

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 automated control and knowledge engineering, and which encompasses many topics involving fuzzy sets and "approximated reasoning."[9]

Industrial applications of fuzzy sets in the context of "fuzzy logic in the wider sense" can be found at fuzzy logic.

Fuzzy number

{{#invoke:main|main}} A fuzzy number is a convex, normalized fuzzy set whose membership function is at least segmentally continuous and has the functional value at precisely one element.

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).

Fuzzy interval

A fuzzy interval is an uncertain set with a mean interval whose elements possess the membership function value . As in fuzzy numbers, the membership function must be convex, normalized, at least segmentally continuous.[10]

Fuzzy relation equation

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.

Axiomatic definition of credibility

[11] 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

Cr{B} indicates how frequently event B will occur.

Credibility inversion theorem

[12] Let A be a fuzzy variable with membership function u. Then for any set B of real numbers, we have

Expected Value

[13] Let A be a fuzzy variable. Then the expected value is


[14] Let A be a fuzzy variable with a continuous membership function. Then its entropy is



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).

The diversity of such constructions and corresponding theories includes: Template:Colbegin

  • interval sets (Moore, 1966),
  • L-fuzzy sets (Goguen, 1967),
  • flou sets (Gentilhomme, 1968),
  • Boolean-valued fuzzy sets (Brown, 1971),
  • type-2 fuzzy sets and type-n fuzzy sets (Zadeh, 1975),
  • set-valued sets (Chapin, 1974; 1975),
  • interval-valued fuzzy sets (Grattan-Guinness, 1975; Jahn, 1975; Sambuc, 1975; Zadeh, 1975),
  • functions as generalizations of fuzzy sets and multisets (Lake, 1976),
  • level fuzzy sets (Radecki, 1977)
  • underdetermined sets (Narinyani, 1980),
  • rough sets (Pawlak, 1982),
  • intuitionistic fuzzy sets (Atanassov, 1983),
  • fuzzy multisets (Yager, 1986),
  • intuitionistic L-fuzzy sets (Atanassov, 1986),
  • rough multisets (Grzymala-Busse, 1987),
  • fuzzy rough sets (Nakamura, 1988),
  • real-valued fuzzy sets (Blizard, 1989),
  • vague sets (Wen-Lung Gau and Buehrer, 1993),
  • Q-sets (Gylys, 1994)
  • shadowed sets (Pedrycz, 1998),
  • α-level sets (Yao, 1997),
  • genuine sets (Demirci, 1999),
  • neutrosophic sets (Smarandache, 1999),
  • soft sets (Molodtsov, 1999),
  • intuitionistic fuzzy rough sets (Cornelis, De Cock and Kerre, 2003)
  • blurry sets (Smith, 2004)
  • L-fuzzy rough sets (Radzikowska and Kerre, 2004),
  • generalized rough fuzzy sets (Feng, 2010)
  • rough intuitionistic fuzzy sets (Thomas and Nair, 2011),
  • soft rough fuzzy sets (Meng, Zhang and Qin, 2011)
  • soft fuzzy rough sets (Meng, Zhang and Qin, 2011)
  • soft multisets (Alkhazaleh, Salleh and Hassan, 2011)
  • fuzzy soft multisets (Alkhazaleh and Salleh, 2012)


See also




  1. L. A. Zadeh (1965) "Fuzzy sets". Information and Control 8 (3) 338–353.
  2. 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 Template:Cite doi
  3. D. Dubois and H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York.
  4. 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.
  5. "Fuzzy Logic: The Logic of Fuzzy Sets"
  6. AAAI
  7. Goguen, Joseph A., 196, "L-fuzzy sets". Journal of Mathematical Analysis and Applications 18: 145–174
  8. Siegfried Gottwald, 2001. A Treatise on Many-Valued Logics. Baldock, Hertfordshire, England: Research Studies Press Ltd., ISBN 978-0-86380-262-1
  9. "The concept of a linguistic variable and its application to approximate reasoning," Information Sciences 8: 199–249, 301–357; 9: 43–80.
  10. "Fuzzy sets as a basis for a theory of possibility," Fuzzy Sets and Systems 1: 3–28
  11. Liu, Baoding. "Uncertain theory: an introduction to its axiomatic foundations." Berlin: Springer-Verlag (2004).
  12. 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.
  13. 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.
  14. Xuecheng, Liu. "Entropy, distance measure and similarity measure of fuzzy sets and their relations." Fuzzy sets and systems 52.3 (1992): 305-318.

Further reading


  • Alkhazaleh, S. and Salleh, A.R. Fuzzy Soft Multiset Theory, Abstract and Applied Analysis, 2012, article ID 350600, 20 p.
  • Alkhazaleh, S., Salleh, A.R. and Hassan, N. Soft Multisets Theory, Applied Mathematical Sciences, v. 5, No. 72, 2011, pp. 3561–3573
  • 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)
  • Atanasov, K. (1986) Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, v. 20, No. 1, pp. 87–96
  • Bezdek, J.C. (1978) Fuzzy partitions and relations and axiomatic basis for clustering, Fuzzy Sets and Systems, v.1, pp. 111–127
  • Blizard, W.D. (1989) Real-valued Multisets and Fuzzy Sets, Fuzzy Sets and Systems, v. 33, pp. 77–97
  • Brown, J.G. (1971) A Note on Fuzzy Sets, Information and Control, v. 18, pp. 32–39
  • Chapin, E.W. (1974) Set-valued Set Theory, I, Notre Dame J. Formal Logic, v. 15, pp. 619–634
  • Chapin, E.W. (1975) Set-valued Set Theory, II, Notre Dame J. Formal Logic, v. 16, pp. 255–267
  • 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
  • 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
  • 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
  • Demirci, M. (1999) Genuine Sets, Fuzzy Sets and Systems, v. 105, pp. 377–384
  • 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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  • Feng F. Generalized Rough Fuzzy Sets Based on Soft Sets, Soft Computing, July 2010, Volume 14, Issue 9, pp 899–911
  • Gentilhomme, Y. (1968) Les ensembles flous en linguistique, Cahiers Linguistique Theoretique Appliqee, 5, pp. 47–63
  • Gogen, J.A. (1967) L-fuzzy Sets, Journal Math. Analysis Appl., v. 18, pp. 145–174
  • Template:Cite doi. Template:Cite doi.
  • Grattan-Guinness, I. (1975) Fuzzy membership mapped onto interval and many-valued quantities. Z. Math. Logik. Grundladen Math. 22, pp. 149–160.
  • 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
  • Gylys, R. P. (1994) Quantal sets and sheaves over quantales, Liet. Matem. Rink., v. 34, No. 1, pp. 9–31.
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  • Jahn, K. U. (1975) Intervall-wertige Mengen, Math.Nach. 68, pp. 115–132
  • 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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  • Kuzmin,V.B. Building Group Decisions in Spaces of Strict and Fuzzy Binary Relations, Nauka, Moscow, 1982 (in Russian)
  • Lake, J. (1976) Sets, fuzzy sets, multisets and functions, J. London Math. Soc., II Ser., v. 12, pp. 323–326
  • 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
  • Miyamoto, S. Fuzzy Multisets and their Generalizations, in 'Multiset Processing', LNCS 2235, pp. 225–235, 2001
  • Molodtsov, O. (1999) Soft set theory – first results, Computers & Mathematics with Applications, v. 37, No. 4/5, pp. 19–31
  • Moore, R.E. Interval Analysis, New York, Prentice-Hall, 1966
  • Nakamura, A. (1988) Fuzzy rough sets, 'Notes on Multiple-valued Logic in Japan', v. 9, pp. 1–8
  • 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
  • Pedrycz, W. Shadowed sets: representing and processing fuzzy sets, IEEE Transactions on System, Man, and Cybernetics, Part B, 28, 103-109, 1998.
  • Radecki, T. Level Fuzzy Sets, 'Journal of Cybernetics', Volume 7, Issue 3-4, 1977
  • 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
  • Salii, V.N. (1965) Binary L-relations, Izv. Vysh. Uchebn. Zaved., Matematika, v. 44, No.1, pp. 133–145 (in Russian)
  • Sambuc, R. Fonctions φ-floues: Application a l'aide au diagnostic en pathologie thyroidienne, Ph. D. Thesis Univ. Marseille, France, 1975.
  • 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.
  • Smarandache, F. (2002) A unifying field in logics: neutrosophic logic, Multiple Valued Logic, v. 8, No. 3, pp. 385–438
  • Smith, N.J.J. (2004) Vagueness and blurry sets, 'J. of Phil. Logic', 33, pp. 165–235
  • Thomas, K.V. and L. S. Nair, Rough intuitionistic fuzzy sets in a lattice, 'International Mathematical Forum', Vol. 6, 2011, no. 27, 1327 - 1335
  • Yager, R. R. (1986) On the Theory of Bags, International Journal of General Systems, v. 13, pp. 23–37
  • 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.
  • Y. Y. Yao, A comparative study of fuzzy sets and rough sets, Information Sciences, v. 109, Issue 1-4, 1998, pp. 227 – 242
  • 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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  • Gianpiero Cattaneo and Davide Ciucci, "Heyting Wajsberg Algebras as an Abstract Environment Linking Fuzzy and Rough Sets" in J.J. Alpigini et al. (Eds.): RSCTC 2002, LNAI 2475, pp. 77–84, 2002. Template:Hide in printTemplate:Only in print


External links

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