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인공지능 2002년 2학기 이복주 단국대학교 컴퓨터공학과

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Presentation on theme: "인공지능 2002년 2학기 이복주 단국대학교 컴퓨터공학과"— Presentation transcript:

1 인공지능 2002년 2학기 이복주 단국대학교 컴퓨터공학과
퍼지 이론 (Lecture Note #14) 인공지능 2002년 2학기 이복주 단국대학교 컴퓨터공학과

2 Outline Fuzzy Logic and Intelligent Systems Progress of Fuzzy Logic
Generalized Modus Ponens Fuzzy Reasoning Triangular Norms Triangular Conorms Theoretical Foundations of Fuzzy Inference

3 Saturn Fuzzy Logic Transmission
Saturn automobile’s smart transmission When the car is moving uphill or downhill Employs shift stabilization Fuzzy logic control is used to avoid hunting, that is frequent shifting of gears Shifting decisions are made by weighing many input variables at once and using fuzzy if-then rules to generate an output control signal

4 Fuzzy Logic and Intelligent Systems
Objective: Develop a cost-effective approximate solution to a problem Approach: Exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low cost Cost of system Utility (Usefulness of system) Precise를 achieve하기 위해서는 cost가 exponential 하게 올라감 반면 utility는 그리 올라가지 않음 Low cost, high usefulness Preciseness

5 Fuzzy Logic Applications
Diagnosis Financial Analysis and Prediction Robotics Data Compression and Pattern Recognition Consumer electronics Typical KB size: rules Typical KB number: 1 Automobile/Transportation system Typical KB size: rules Typical KB number: 1-3 Locomotive, Subways, Aircraft engines, Helicopters Typical KB size: rules Typical KB number: supervisory (hierarchical)

6 Progress of Fuzzy Logic
1965, Prof. Loft A. Zadeh at UCB developed fuzzy set theory and fuzzy logic 1974, Fuzzy logic controller for a steam engine (Prof. Mamdani, London University) 1980, Control of Cement-Kiln with monitor capability (Smidth, Denmark) 1987, Automatic train operation for Sendai subway (Hitachi) 1988, Stock trading expert system (Yamichi security) 1989, Laboratory for International Fuzzy Engineering , 389 patents in U.S. regarding fuzzy systems

7 Fuzzy Logic is Not … Is not a clever disguise of probability theory
The behavior of a fuzzy system is not fuzzy (it is deterministic) The founder of fuzzy logic (Prof. Zadeh) is not a mathematician. Fuzzy logic is not to replace conventional techniques.

8 Generalized Modus Ponens
In fuzzy logic the truth value of a statement becomes a matter of degrees Reasoning in fuzzy logic is based on generalized modus ponens Modus Ponens Generalized Modus Ponens Given A  B Given A  B A A’ Deduce B Deduce B’ where A’ is a fuzzy set that partially matches A Example If a person is self-confident then he/she has a happy life Jack is somewhat self-confident What can we conclude using conventional logic? What can we conclude using generalized modus ponens? => Jack is somewhat happy None

9 Fuzzy Reasoning Fuzzy Rules: If x is A1 and y is B1 then z is C1
x and y are inputs, z is an output Ai, Bi, and Ci are fuzzy sets Input data: x is A’, y is B’ A’ and B’ are also fuzzy sets Question: z is ?

10 Step 1: Compatibility Calculate the degree that input data (A’, B’) matches each rule premise (A1, A2, B1, B2) (A1, A’), (A2, A’), (B1, B’), (B2, B’) Compatibility  between A and A’: (A, A’) = supx min{A(x), A’(x)} Supreme: maximum in continuous space 1 A A’ 0.5 x

11 Step 2: Combine Compatibilities
Combine the degree of matching for the inputs for “and”, usually take min for “or”, usually take max min{(A1, A’), (B1, B’)} min{(A2, A’), (B2, B’)} x 0.5 1 A’ y 0.3 B’ A B

12 Step 3: Derive Output Fuzzy Sets
The (combined) degree of matching i is propagated to the consequent to form an inferred fuzzy subset Ci’ Type I: C’(z) =   C(z) [ usually take min ] Type II: C’(z) =  x C(z) C1’ and C2’ are derived z 1 C 0.3 C’ Type I Type II

13 Step 4: Combine Output Fuzzy Sets
Combine the inferred fuzzy values (C1’ and C2’) of z max {C1’(z), C2’(z)} z 1 C2’ C1’

14 Step 5: Defuzzification
Perform defuzzification to obtain z’s final value Mean of Maximum method (MOM) (j=1,kwj)/k where wj is peak and k is the number of peaks Center of Area method (COA) (j=1,nz(wj) x wj)/ j=1,nz(wj) w1 1 wk MOM 1 COA w1 wn

15 What about Crisp Input? Inputs are x0 and y0 rather than A’ and B’.
Compatibility  between A and x0 (Step 1): (A, x0) = A(x0) 1 A supreme 0.5 x0

16 Triangular Norms (Conjunction)
Four methods to calculate “and” Min  A and B (x) = min{A(x), B(x)} Algebraic product  A and B (x) = A(x) x B(x) Bounded difference  A and B (x) = max {0, A(x) + B(x) – 1} Drastic product  A and B (x) = A(x) if B(x) = 1 B(x) if A(x) = 1 0 otherwise min  algebraic product  bounded difference  drastic product

17 Triangular Conorms (Disjunction)
Four methods to calculate “or” Max  A OR B (x) = max{A(x), B(x)} Bounded sum  A OR B (x) = min{1,A(x) + B(x)} Algebraic sum  A OR B (x) = A(x) + B(x) - A(x) x B(x) Drastic sum  A OR B (x) = A(x) if B(x) = 0 B(x) if A(x) = 0 1 otherwise max  bounded sum  algebraic sum  drastic sum

18 Theorectical Foundation of Fuzzy Inference
A fuzzy rule = A fuzzy relation Inference is a composition of relations Relation R: U X V = {0, 1} R(x, y) = 1: the relation holds between x and y R(x, y) = 0: the relation does not hold between x and y e.g., Take(Kim, ICE607) = 1 Take(Kim, ICE608) = 0 Fuzzy relation R: U X V = [0, 1] E.g., Friendly(Jack, Joe) = 1 Friendly(Clinton, Hussain) = 0 Friendly(Clinton, Gingrich) = 0.2

19 Fuzzy Rule as Fuzzy Relation
Fuzzy rules are fuzzy relations over the Cartesian product of the domains of antecedent and consequent variables Semantically the fuzzy relation captures the degree of association between a pair of antecedent variables and consequent variables Example If height is tall then IQ is high High H\I R(6.5,135) Tall R(6.5,135): The possibility that a person 6.5 tall has a 135 IQ.

20 Methods to Calculate the Degree of Association
Two families of calculating the degree of association Fuzzy implication operators Fuzzy conjunction operators

21 Families of Fuzzy Implication Operators
Material implication A  B = (not A) + B e.g., Tall(6.25)=0.25, High(125)=0.5 TallHigh(6.25,125)=max{1-0.25,0.5}=0.75 Propositional calculus A  B = (not A) + (A * B) ( A  (AB) = (AA)  (AB) = AB ) Extended propositional calculus A  B = (A x B) + B

22 Families of Fuzzy Implication Operators (2)
Generalization of modus ponens A  B = sup { c[0,1] | A x c  B } e.g., Tall(6.25)=0.25, High(125)=0.5 TallHigh(6.25,125) = sup { c[0,1] | min(0.25,c)  0.5 } = 1 If x is min, generalization of mp is A  B = 1 if A  B B otherwise Generalization of modus tollens A  B = inf { t[0,1] | B+t  A } All fuzzy implication operations share the fundamental implication property 00=1, 01=1, 10=0, 11=1

23 Fuzzy Relation Using Conjunction Operator
Use conjunction to represent relation (implication) A  B = min {A, B} i.e., AB(x, y) = min {A(x), B(y)} Does this hold the fundamental implication property? 00=1, 01=1, 10=0, 11=1

24 Summary Fuzzy Logic and Intelligent Systems Progress of Fuzzy Logic
Generalized Modus Ponens Fuzzy Reasoning Triangular Norms Triangular Conorms Theoretical Foundations of Fuzzy Inference

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