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Lecture 4 (Fuzzy Set Operations)

http://expertsys.4t.com. Lecture 4 (Fuzzy Set Operations). “We need a radically different kind of mathematics, the mathematics of fuzzy or cloudy quantities which are not describable in terms of probability distributions” L.A.Zadeh, 1962. Some points of the previous lecture.

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Lecture 4 (Fuzzy Set Operations)

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  1. http://expertsys.4t.com Lecture 4 (Fuzzy Set Operations) “We need a radically different kind of mathematics, the mathematics of fuzzy or cloudy quantities which are not describable in terms of probability distributions” L.A.Zadeh, 1962 Lecture 04

  2. Some points of the previous lecture • Fuzzy Logic is some kind of multi-valued logic. Unlike crisp two valued logic, the truth value in fuzzy logic can be any number between 0 and 1 and hence is an extension to classical logic • Consequently, many of the Natural Language propositions can be represented with fuzzy logic Lecture 04

  3. A fuzzy set (A generalized concept of the conventional crisp set) is specified with a membership functionA(x) which represents degree of membership in the set. • A ={ (x,A(x)) | xU , 0A(x)  1} Lecture 04

  4. Basic Set-Theoretic Operations • Equality: • Subset: • Complement: • Union: • Intersection: Slides for fuzzy sets, J.-s. Roger Jang Lecture 04

  5. De Morgan’s Laws Set operations on fuzzy sets are supposed to be defined such that the more previous known laws and equalities remain true in fuzzy sets as well. Having the previous definitions for fuzzy set operations, we can verify De Morgan laws in fuzzy logic as well. Exercise: Prove the above equalities Lecture 04

  6. Fuzzy Set operations in details - Fuzzy Complement • Fuzzy complement is actually a function say c that maps the membership function to the membership function of the complement set . Definition:Any function c :[0,1][0,1] that satisfies the following Axioms c1 and c2 is called a fuzzy complement Lecture 04

  7. Requirements • Axiom c1. (boundary conditions) • Axiom c2. (non-increasing condition) Axiom c1 requires that if an element belongs to a fuzzy set to degree zero (one), then it should belong to the complement of this fuzzy set to degree one (zero). Axiom c2 means that an increase in membership value of a fuzzy set must result in a decrease or no change in membership value of the complement set Lecture 04

  8. Clearly, in classical crisp logic (where domain of definition of the complement function is {0,1}) there is only one complement function which satisfies the above axioms whereas in fuzzy logic, there are many functions with a domain [0,1] which satisfy the above conditions. Examples of fuzzy complements 1. Basic Fuzzy Complement Lecture 04

  9. 2. Sugeno class of fuzzy complements For any value of the parameter , a particular fuzzy complement function is obtained 3. Yager class of fuzzy complements For any value of the parameter , a particular fuzzy complement function is obtained Lecture 04

  10. Graphical Representation of the Sugeno Class Complement Lecture 04

  11. Graphical Representation of the Yager Class Complement Lecture 04

  12. - Fuzzy Union s-norm (t-conorm) • Intuitively, the union of two sets, AB means a fuzzy set (in particular the smallest one) containing both A and B. • The union of two fuzzy sets can be defined with a function named s-norm s:[0,1]x[0,1][0,1] which maps the membership functions of fuzzy sets A and B into the membership function of the union of A and B • (called AB) The requirements for a function to be an s-norm are as follow: Lecture 04

  13. Axiom s1. (boundary conditions) • Axiom s2. (commutative condition) • Axiom s3. (non-decreasing condition) • Axiom s4. (associative condition) Lecture 04

  14. Definition:Any function s:[0,1]x[0,1][0,1] that satisfies the above 4 axioms is called an s-norm Examples of fuzzy s-norms 1. Dombi calss 2. Dubois-Prade calss Lecture 04

  15. 3. Yager calss 4. Drastic Sum: 5. Einstein Sum: Lecture 04

  16. 6. Algebraic Sum: 7. Maximum (Basic fuzzy Union) Theorem S1: For any s-norm, s(a,b) the following inequality holds: (for any a,b  [0,1] It means that the smallest s-norm (or smallest union of two fuzzy sets) is maximum while the largest s-norm is Drastic sum Lecture 04

  17. Theorem S2: Dombi s-norm and Yager s-norm cover the whole spectrum of s-norms when their parameters change In its extreme cases: And Also So it is possible to build any s-norm with choosing the right parameter in any of the yager or dombi s-norms Lecture 04

  18. - Fuzzy Intersection t-norm • Intuitively, the intersection of two sets, AB means a fuzzy set (in particular the largest one) containing by both A and B. • The Intersection of two fuzzy sets can be defined with a function named t-norm t:[0,1]x[0,1][0,1] which maps the membership functions of fuzzy sets A and B into the membership function of the intersection of A and B The requirements for a function to be a t-norm are as follow: Lecture 04

  19. Axiom t1. (boundary conditions) • Axiom t2. (commutative condition) • Axiom t3. (non-decreasing condition) • Axiom t4. (associative condition) Lecture 04

  20. Definition:Any function t:[0,1]x[0,1][0,1] that satisfies the above 4 axioms is called a t-norm Examples of fuzzy t-norms 1. Dombi calss 2. Dubois-Prade calss Lecture 04

  21. 3. Yager calss 4. Drastic Product: 5. Einstein Product: Lecture 04

  22. 6. Algebraic Product: 7. Minimum(Basic fuzzy Intersection) Theorem T1: For any t-norm, t(a,b) the following inequality holds: (for any a,b  [0,1] ) It means that the largest t-norm (or largest intersection of two fuzzy sets) is minimum while the smallest t-norm is Drastic product Lecture 04

  23. Theorem T2: Dombi t-norm and Yager t-norm cover the whole spectrum of t-norms when their parameters change In its extreme cases: And also: So it is possible to build any t-norm with choosing the right parameter in any of the yager or dombi t-norms. Lecture 04

  24. Yager: S(a,b)=sw (a,b) W=3 Graphical representation of theorem S1 and S2 Algebraic sum: S(a,b)=sas (a,b) S(a,b)=max(a,b) Lecture 04

  25. Yager: t(a,b)=tw (a,b) W=3 Graphical representation of theorem T1 and T2 S(a,b)=min(a,b) Algebraic product: S(a,b)=sap (a,b) Lecture 04

  26. Generalized De Morgan’s Law • Using the new definitions of s-norm and t-norm instead of the basic fuzzy union and basic fuzzy intersection respectively, the generalized De Morgan’s Law can be shown as follow: c( t(a,b) ) = s( c(a), c(b) ) c( s(a,b) ) = t( c(a), c(b) ) where c(.) denotes for any fuzzy complement and s(.) and t(.) denote for fuzzy s-norm and fuzzy t-norm respectively. Lecture 04

  27. Associated class An s-norm s(a,b), a t-norm t(a,b) and a fuzzy complement c(a) form an associated class if they all together satisfy the Generalized De Morgan laws c[s(a,b)]=t[c(a),c(b)] It can be shown that there is a t-norm associated with each s-norm in the sense that there is a complement such that the De Morgan laws are satisfied. For example the Yager s-norm and t-norms are associated with each other through basic fuzzy complement Lecture 04

  28. References • 1. L.X. Wang, A course in Fuzzy Systems and control • 2. Tutorial on Fuzzy Logic, Jan Jantzen, Technical University of Denmark, Technical report no 98-E 868, 1999 • 3. Slides for fuzzy sets, J.-s. Roger Jang http://www.cs.nthu.edu.tw/~jang Lecture 04

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