1. Chapter 9 Fuzzy Inference

2. 9.1 Composition of rules 1) Composition of crisp sets A and B.
It can represent a relation R between the sets A and B. R = {(x, y) | x  A, y  B}, R  A  B
2) Composition of fuzzy sets A and B. It is a relation R between A and B.
R = {((x, y), R(x, y)) | R(x, y) = min[A(x), B(y)] or R(x, y) = A(x)  B(y)}
3) Composition of crisp relations R and S
S  R = {(x, z) | (x, y)  R, (y, z)  S}
where R  A  B, S  B  C, and S  R  A  C
4) Composition of fuzzy relations R and S
SR = S  R = {((x, y), SR(x, z))}
where

3. 9.1 Composition of rules
1) Fuzzy conjunction: the Cartesian product on X and Y is
interpreted as a fuzzy conjunction defined by
where  is an operator representing a t-norm (triangle-norm), x  X, y  Y, A  X, and B  Y
2) Fuzzy disjunction: the Cartesian product on X  Y is
interpreted as a disjunction defined by
whereis an operator representing a t-conorm (or s-norm),
x  X, y  Y, A  X, and B  Y.

4. 9.1 Composition of rules The max-min composition
R1R2 = {((x, z), R1R2(x, z))}
where R1R2(x, z) = [R1(x, y), R2(y, z)]
= [R1(x, y)  R2(y, z)]
x  X, y  Y, z  Z, R1  X  Y, R2  Y  Z
The max-product composition
R1  R2 = {((x, z), R1R2(x, z))}
where R1R2(x, z) = [R1(x, y)  R2(y, z)]
x  X, y  Y, z  Z, R1  X  Y, R2  Y  Z

5. Example 9.1 consider a fuzzy rule in the following.
“x and y are approximately equal.”
premise is given like “x is small.”
R(x, y) = Approximately_Equal(x, y)
R(x) = Small(x) x x 1 2 3 4
R(x)
0.6
0.2
Membership degrees of R(x) y x 1 2 3 4
Membership degrees of R(x, y) 1 1
0.5 2
0.5 1
0.5 3
0.5 1
0.5 4
0.5 1

6. Example 9.1 let’s use the max-min composition operator.
R(y) = R(x)  R(x, y) R(y) = [R(x)  R(x, y)] y 1 2 3 4
R(y)
0.6
0.5
0.2
Membership degrees of R(y)

7. 9.2 Fuzzy rules and implication
R = A  B
R can be viewed as a fuzzy set with a two-dimensional
membership function
R(x, y) = f(A(x), B(y))
Min operation rule of fuzzy implication
RC = A  B = A(x)  B(y) / (x, y)
where  is the min operator
Product operation rule of fuzzy implication
RP = A  B = A(x)  B(y) / (x, y)
where  is the algebraic product operator

8. Example 9.3 fuzzy rule in the following.
“If temperature is high, then humidity is fairly high”
R(t, h): If t is A, then h is B.
R(t, h): R(t)  R(h)
R(t): t is A R(h): h is B t 20 30 40
A(t)
0.1
0.5
0.9 h 20 50 70 90
B(h)
0.2
0.6
0.7 1
Membership of A in T (temperature)
Membership degrees of B in H (humidity)

9. Example 9.3 RC(t, h) = A  B = A(t)  B(h) / (t, h) h 20 50 70 90 t
0.1
0.1
0.1
0.1 30
0.2
0.5
0.5
0.5 40
0.2
0.6
0.7
0.9

10. Example 9.4
we want to get information about the humidity when there is the following premise about the temperature
“Temperature is fairly high”
R(t): “t is A” where A = “fairly high” t 20 30 40
A(t)
0.01
0.25
0.81
Membership function of A in T (temperature)

11. Example 9.4 R(h) = R(t)  RC(t, h) h 20 50 70 90 B(h) 0.2 0.6 0.7
0.81
Result of fuzzy inference

12. 9.3 Inference mechanism 9.3.1 Decomposition of rule base
The rule base has the form of a MIMO (multiple input
multiple output) system.
: (Ai  …  Bi)  (z1 + … + zq)

13. 9.3.1 Decomposition of rule base

14. 9.3.2 Two-input/single-output rule base
Ri: If u is Ai and v is Bi then w is Ci
Ri: (Ai and Bi)  Ci
where “Ai and Bi” is a fuzzy set Ai  Bi in U  V.
Ri: (Ai and Bi)  Ci is a fuzzy implication relation in U  V  W, and  denotes a fuzzy implication function

15. 9.3.3 Compositional rule of inference
consider a single fuzzy rule and its inference
R1: if v is A then w is C
Input: v is A Result: C
A  U, C  W, v  U, and w  C.
input A is given to the inference system,
The output C C = A  R1

16. 9.3.3 Compositional rule of inference
Lemma 1 (For 1 singleton input, result C is obtained from C and matching degree 1 )
When a fuzzy rule R1 and singleton input u0 are given
R1: If u is A then w is C, Or R1: A  C
inference result C is defined by the membership function C(w)
C(w) = 1  C(w) for RC (Mamdani implication)
C(w) = 1  C(w) for RP (Larsen implication)
where 1 = A(u0) A C u w A C u0 1 C
Graphical representation of Lemma 1 with RC

17. 9.3.3 Compositional rule of inference
Lemma 2: (For 1 fuzzy input, result C is obtained from C and matching degree 1 )
When a fuzzy rule R1: A  C and input A are given, the inference result C is defined by the membership function C.
C (w) = 1  C(w) for RC
C (w) = 1  C(w) for RP
where 1 =[A (u)  A(u)] A C u w A C 1 C A
Graphical representation of Lemma 2 with RC

18. 9.3.4 Fuzzy inference with rule base
Lemma 3: (Total result C is an aggregation of individual results,)
The result of inference C is an aggregation of resultderived from individual rules
C(w) w C1 1
C1
C(w) w C2 2
C2
R1: A1  C1
R2: A2  C2 w C
Lemma 3 (Total result C is a union of individual result Ci )

19. 9.3.4 Fuzzy inference with rule base
Lemma 4: (Ri: (Ai  Bi  Ci) consists of Ri1: (Ai  Ci) and Ri2: (Bi  Ci))
When there is a rule Ri with two inputs variables Ai and Bi, the inference result Ci is obtained from individual inferences of Ri1: (Ai  Ci) and Ri2: (Bi  Ci).

20. 9.3.4 Fuzzy inference with rule baseAi Bi Ci u v w
Ri: Ai  Bi  Ci Ai Ci A
Ci1 u w
Ri1: Ai  Ci Ci B Bi
Ci2 v w
Ci w
Ri2: Bi  Ci
Lemma 4 (Rule Ri can be decomposed into Ri1 and Ri2 and the result Ci of Ri is an intersection of the results Ci1 and Ci2 of Ri1 and Ri2, respectively.)

21. 9.3.4 Fuzzy inference with rule base
Lemma 5: (For singleton input,is determined by the minimum
matching degree of Ai and Bi)
If the inputs are fuzzy singletons, namely, A = u0, B = v0, the matching degree i is the minimum value between . From the lemma 1, the inference result can be derived by employing Mamdani’s minimum operation rule RC and Larsen’s product operation rule RP for the implication

22. 9.3.4 Fuzzy inference with rule baseAi Bi Ci u v w u0 v0
min i
Lemma 5 (i is the minimum matching degree between Ai(u0) and Bi(v0).)

23. 9.3.4 Fuzzy inference with rule base
Lemma 6: (For fuzzy input, Ci is determined by the minimum matching degree of (A and Ai) and (B and Bi)
If the inputs are given as fuzzy sets A and B, the matching degree i is determined by the minimum between (A and Ai) and (B and Bi). From the lemma 2, the results can be derived by employing the min operation for RC and the product operation for RP

24. 9.3.4 Fuzzy inference with rule baseAi Bi Ci u v w
min i A B
Lemma 6 (i is the minimum matching degree between (A and Ai) and (B and Bi).)

25. 9.4 Inference methods 9.4.1 Mamdani method
This method uses the minimum operation RC as a fuzzy implication and the max-min operator for the composition
Let’s suppose a rule base is given in the following form
Ri: if u is Ai and v is Bi then w is Ci, i = 1, 2, …, n
for u  U, v  V, and w  W.
Then, Ri = (Ai and Bi)  Ci is defined by

26. 9.4.1 Mamdani method When input data are singleton u = u0, v = v0
From lemma 5,

27. 9.4.1 Mamdani method 1 u v w
min u0 v0 1 2
C1
C2
Graphical representation of Mamdani method with singleton input

28. 9.4.1 Mamdani method When input data are fuzzy sets, A and B
From Lemma 6
From Lemma 3

29. 9.4.1 Mamdani method 1 u v w
min 1 2 A1 A B B1 C1 A2 B2 C2
Graphical interpretation of Mamdani method with fuzzy set input

30. Example 9.5 fuzzy rulebase including one rule such as :
R: If u is A then v is B
Where A=(0, 2, 4) and B=(3, 4, 5) are triangular fuzzy sets.
given as singleton value u0=3, how can we calculate the output B using the Mamdani method? 2 3 4 u0 1
0.5 B B 5 2 3 4 u0 1
0.5 B B 5
Fuzzy inference with input u0=3
Fuzzy inference with input A=(0, 1, 2).

31. 9.4.2 Larsen method
This method uses the product operator RP for the fuzzy implication and the max-product operator for the composition
Ri: if u is Ai and v is Bi then w is Ci, i = 1, 2, … , n
Then
is defined by

32. 9.4.2 Larsen method
When the singleton input data are given as u = u0, v = v0
from Lemma 5
From Lemma 3

33. 9.4.2 Larsen method 1 u v w
min 1 2
Graphical representation of Larsen method with singleton input

34. 9.4.2 Larsen method
When the input data are given as the form of fuzzy sets A and B,
from Lemma 6
From Lemma 3

35. 9.4.2 Larsen method 1 u v w
min 1 2
Graphical representation of Larsen method with fuzzy set input

36. Example 9.6 There is a fuzzy rule : R : if u is A and v B then w is C
Where A=(0, 2, 4), B=(3, 4, 5) and C=(3, 4, 5)
Find inference result C when input is u0 =3, v0=4 by
using Larsen method
Find inference result C when input is A=(0, 1, 2) and
B=(2, 3, 4).

37. Example 9.6 Larsen method with input u0 =3, v0=41
0.5 2 3 4 u0 A 5 v0 B C
Larsen method with input u0 =3, v0=4 2 4 1
0.5 A A 3 C 5 B B
2/3
Larsen method with input A=(0, 1, 2), B=(2, 3, 4).

38. 9.4.3 Tsukamoto method
the consequence of each fuzzy rule is represented by a fuzzy set with a monotonic membership function
The rule base has the form as:
Ri: if u is Ai and v is Bi, then w is Ci, i = 1, 2, … , n
where is a monotonic function.

39. 9.4.3 Tsukamoto method Graphical representation of Tsukamoto method
Min  u v w A1 B1 C1 A2 B2 C2 u0 1 w2 w1 2
Weighted Average v0
Graphical representation of Tsukamoto method

40. 9.4.4 TSK method Graphical representation of TSK method Min  u v A1B1 A2 B2 u0 1 2
w1=p1u0+q1v0+r1
w2=p2u0+q2v0+r2
Weighted Average v0
Graphical representation of TSK method