Download presentation

Presentation is loading. Please wait.

Published bySean Flores Modified over 3 years ago

1
صفحه 1 Spring 2002 Lecture 04 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

2
صفحه 2 Spring 2002 Lecture 04 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 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 Consequently, many of the Natural Language propositions can be represented with fuzzy logic

3
صفحه 3 Spring 2002 Lecture 04 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 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} A ={ (x, A (x)) | x U, 0 A (x) 1}

4
صفحه 4 Spring 2002 Lecture 04 Basic Set-Theoretic Operations Equality: Equality: Subset: Subset: Complement: Complement: Union: Union: Intersection: Intersection: Slides for fuzzy sets, J.-s. Roger Jang

5
صفحه 5 Spring 2002 Lecture 04 De Morgans 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

6
صفحه 6 Spring 2002 Lecture 04 Fuzzy Set operations in details Fuzzy complement is actually a function say c that maps the membership function to the membership function of the complement set. - Fuzzy Complement Definition: Any function c :[0,1] [0,1] that satisfies the following Axioms c1 and c2 is called a fuzzy complement

7
صفحه 7 Spring 2002 Lecture 04 Requirements Requirements Axiom c1. (boundary conditions) Axiom c1. (boundary conditions) Axiom c2. (non-increasing condition) 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

8
صفحه 8 Spring 2002 Lecture 04 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

9
صفحه 9 Spring 2002 Lecture 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

10
صفحه 10 Spring 2002 Lecture 04 Graphical Representation of the Sugeno Class Complement

11
صفحه 11 Spring 2002 Lecture 04 Graphical Representation of the Yager Class Complement

12
صفحه 12 Spring 2002 Lecture 04 Intuitively, the union of two sets, A B means a fuzzy set (in particular the smallest one) containing both A and B. 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 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) (called A B) - Fuzzy Union s-norm (t-conorm) The requirements for a function to be an s-norm are as follow:

13
صفحه 13 Spring 2002 Lecture 04 Axiom s1. (boundary conditions) Axiom s1. (boundary conditions) Axiom s2. (commutative condition) Axiom s2. (commutative condition) Axiom s3. (non-decreasing condition) Axiom s3. (non-decreasing condition) Axiom s4. (associative condition) Axiom s4. (associative condition)

14
صفحه 14 Spring 2002 Lecture 04 Definition: Any function s:[0,1]x[0,1] [0,1] that satisfies the above 4 axioms is called an s-norm 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

15
صفحه 15 Spring 2002 Lecture Yager calss 4. Drastic Sum: 5. Einstein Sum:

16
صفحه 16 Spring 2002 Lecture 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

17
صفحه 17 Spring 2002 Lecture 04 Theorem S2: Dombi s-norm and Yager s-norm cover the whole spectrum of s-norms when their parameters change Theorem S2: Dombi s-norm and Yager s-norm cover the whole spectrum of s-norms when their parameters change In its extreme cases: So it is possible to build any s-norm with choosing the right parameter in any of the yager or dombi s-norms And Also

18
صفحه 18 Spring 2002 Lecture 04 Intuitively, the intersection of two sets, A B means a fuzzy set (in particular the largest one) containing by both A and B. 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 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 - Fuzzy Intersectiont-norm The requirements for a function to be a t-norm are as follow:

19
صفحه 19 Spring 2002 Lecture 04 Axiom t1. (boundary conditions) Axiom t1. (boundary conditions) Axiom t2. (commutative condition) Axiom t2. (commutative condition) Axiom t3. (non-decreasing condition) Axiom t3. (non-decreasing condition) Axiom t4. (associative condition) Axiom t4. (associative condition)

20
صفحه 20 Spring 2002 Lecture 04 Definition: Any function t:[0,1]x[0,1] [0,1] that satisfies the above 4 axioms is called a t-norm 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

21
صفحه 21 Spring 2002 Lecture Yager calss 4. Drastic Product: 5. Einstein Product:

22
صفحه 22 Spring 2002 Lecture 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

23
صفحه 23 Spring 2002 Lecture 04 Theorem T2: Dombi t-norm and Yager t-norm cover the whole spectrum of t-norms when their parameters change 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.

24
صفحه 24 Spring 2002 Lecture 04 Graphical representation of theorem S1 and S2 S(a,b)=max(a,b) Yager: S(a,b)=s w (a,b) W=3 Algebraic sum: S(a,b)=s as (a,b)

25
صفحه 25 Spring 2002 Lecture 04 Graphical representation of theorem T1 and T2 S(a,b)=min(a,b) Yager: t(a,b)=t w (a,b) W=3 Algebraic product: S(a,b)=s ap (a,b)

26
صفحه 26 Spring 2002 Lecture 04 Generalized De Morgans 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 Morgans Law can be shown as follow: Using the new definitions of s-norm and t-norm instead of the basic fuzzy union and basic fuzzy intersection respectively, the generalized De Morgans 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.

27
صفحه 27 Spring 2002 Lecture 04 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

28
صفحه 28 Spring 2002 Lecture 04 References 1. L.X. Wang, A course in Fuzzy Systems and control 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, Tutorial on Fuzzy Logic, Jan Jantzen, Technical University of Denmark, Technical report no 98-E 868, Slides for fuzzy sets, J.-s. Roger Jang 3. Slides for fuzzy sets, J.-s. Roger Jang

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google