Pushpak Bhattacharyya CSE Dept IIT Bombay Lecture4 Fuzzy Inferencing 29 th July 2010 Meaning of fuzzy subset Suppose following classical set theory we say if Consider the nhyperspace representation of A and B ID: 534034
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Slide1
CS621: Introduction to Artificial Intelligence
Pushpak Bhattacharyya
CSE Dept.,
IIT Bombay
Lecture–4: Fuzzy
Inferencing
29
th
July 2010Slide2
Meaning of fuzzy subset
Suppose, following classical set theory we say
if Consider the n-hyperspace representation of A and B
(1,1)
(1,0)
(0,0)
(0,1)
x
1
x
2
A
.
B
1
.
B
2
.
B3
Region where Slide3
This effectively means
CRISPLY
P(A) = Power set of AEg: Suppose A = {0,1,0,1,0,1,…………….,0,1} – 10
4 elements
B = {0,0,0,1,0,1,……………….,0,1} – 104 elements
Isn’t with a degree? (only differs in the 2nd element) Slide4
Subset operator is the “odd man” outAUB, A∩B, Ac
are all “Set Constructors” while
A B is a Boolean Expression or predicate.According to classical logicIn Crisp Set theory A B is defined as
x xA xBSo, in fuzzy set theory A
B can be defined as x µ
A(x)
µB(x)Slide5
Zadeh’s definition of subsethood goes against the grain of fuzziness theory
Another way of defining A
B is as follows: x µ
A(x) µ
B(x)
But, these two definitions imply that µP(B)
(A)=1 where P(B) is the power set of B
Thus, these two definitions violate the fuzzy principle that every belongingness except Universe is fuzzySlide6
Fuzzy definition of subset
Measured in terms of “fit violation”, i.e. violating the condition
Degree of subset hood S(A,B)= 1- degree of superset
=
m(B)
= cardinality of B
= Slide7
We can show that
Exercise 1:
Show the relationship between entropy and subset hoodExercise 2:Prove that
Subset hood of B in ASlide8
Fuzzy sets to fuzzy logic
Forms the foundation of fuzzy rule based system or fuzzy expert system
Expert SystemRules are of the formIf
then
AiWhere
Cis are conditions
Eg: C1
=Colour of the eye yellowC2
= has feverC
3=high bilurubinA = hepatitis Slide9
In fuzzy logic we have fuzzy predicates
Classical logic
P(x1,x2,x3…..x
n) = 0/1Fuzzy Logic
P(x1,x
2,x3
…..xn) = [0,1]
Fuzzy OR
Fuzzy AND
Fuzzy NOTSlide10
Fuzzy ImplicationMany theories have been advanced and many expressions exist
The most used is Lukasiewitz formula
t(P) = truth value of a proposition/predicate. In fuzzy logic t(P) = [0,1]t( ) = min[1,1 -t(P)+t(Q)]
Lukasiewitz definition of implicationSlide11
Linguistic VariablesFuzzy sets are named by Linguistic Variables (typically adjectives).Underlying the LV is a numerical quantity
E.g. For ‘tall’ (LV), ‘height’ is numerical quantity.
Profile of a LV is the plot shown in the figure shown alongside.
μ
tall
(h)
1 2 3 4 5 6
0
height
h
1
0.4
4.5Slide12
Example Profiles
μ
rich
(w)
wealth w
μ
poor
(w)
wealth wSlide13
Example Profiles
μ
A
(x)
x
μ
A
(x)
x
Profile representing
moderate (
e.g.
moderately rich)
Profile representing
extremeSlide14
Concept of HedgeHedge is an intensifierExample:
LV = tall, LV
1 = very tall, LV2 = somewhat tall‘very’ operation: μ
very tall(x) =
μ2
tall(x)‘somewhat’ operation:
μ
somewhat tall(x) =
√(μ
tall(x))
1
0
h
μ
tall
(h)
somewhat tall
tall
very tallSlide15
Fuzzy InferencingTwo methods of inferencing in classical logicModus Ponens
Given
p and pq, infer qModus TolensGiven ~q and p
q, infer ~pHow is fuzzy inferencing done?Slide16
A look at reasoningDeduction: p, p
q
|- qInduction: p1, p2, p3, …|- for_all p
Abduction: q, pq|- p
Default reasoning: Non-monotonic reasoning: Negation by failureIf something cannot be proven, its negation is asserted to be trueE.g., in PrologSlide17
Fuzzy Modus Ponens in terms of truth valuesGiven t(p)=1 and
t(
pq)=1, infer t(q)=1In fuzzy logic, given
t(p)>=a, 0<=a<=1and t(p>q)=c, 0<=c<=1What is
t(q)How much of truth is transferred over the channel
p
qSlide18
Lukasiewitz formulafor Fuzzy Implication
t(P) = truth value of a proposition/predicate. In fuzzy logic t(P) = [0,1]
t( ) = min[1,1 -t(P)+t(Q)]
Lukasiewitz definition of implicationSlide19
Use Lukasiewitz definition
t(
pq) = min[1,1 -t(p)+t(q)]We have t(p->q)=c, i.e., min[1,1 -t(p)+t(q)]=c
Case 1:c=1
gives 1 -t(p)+t(q)>=1, i.e., t(q)>=aOtherwise,
1 -t(p)+t(q)=c, i.e., t(q)>=c+a-1Combining,
t(q)=max(0,a+c-1)This is the amount of truth transferred over the channel
pqSlide20
Two equations consistent
These two equations are consistent with each otherSlide21
ProofLet us consider two crisp sets A and B
1
U
A
B
2
U
B
A
3
U
A
B
4
U
A
BSlide22
Proof (contd…)Case I:So,Slide23
Proof (contd…)Thus, in case I these two equations are consistent with each other
Prove them for other three casesSlide24
Eg: If pressure is high then Volume is low
Pressure/Volume
High Pressure
ANDING of Clauses on the LHS of implication
Low VolumeSlide25
An Example
Controlling
an inverted pendulum:
θ
= angular velocity
Motor
i=currentSlide26
The goal: To keep the pendulum in vertical position (θ
=0)
in dynamic equilibrium. Whenever the pendulum departs from vertical, a torque is produced by sending a current ‘i’Controlling factors for appropriate currentAngle θ
, Angular velocity θ.
Some intuitive rules
If θ is +ve small and
θ.
is –ve smallthen current is zero
If θ is +ve small and
θ. is +ve small
then current is –ve mediumSlide27
-ve med
-ve small
Zero
+ve small
+ve med
-ve med
-ve small
Zero
+ve small
+ve med
+ve med
+ve small
-ve small
-ve med
-ve small
+ve small
Zero
Zero
Zero
Region of interest
Control Matrix
θ
.
θSlide28
Each cell is a rule of the form
If
θ is <> and θ. is <>
then i is <>4 “Centre rules”
if θ = = Zero and
θ.
= = Zero then i = Zeroif
θ is +ve small and θ
. = = Zero then i is –ve small
if θ is –ve small and θ
.= = Zero then i is +ve smallif θ
= = Zero and θ. is
+ve small then i is –ve small
if θ = = Zero and θ
. is –ve small then i is +ve smallSlide29
Linguistic variables
Zero
+ve small-ve smallProfiles
-
ε
+
ε
ε
2
-
ε
2
-
ε
3
ε
3
+ve small
-ve small
1
Quantity (
θ
,
θ
.
, i)
zeroSlide30
Inference procedureRead actual numerical values of
θ
and θ.Get the corresponding
μ values
μZero,
μ(+ve small)
, μ
(-ve small). This is called FUZZIFICATION
For different rules, get the fuzzy I-values from the R.H.S of the rules.“
Collate” by some method and get ONE
current value. This is called DEFUZZIFICATIONResult is one numerical value of
‘i’.Slide31
if θ is Zero and dθ/dt is Zero then i is Zero
if
θ is Zero and dθ/dt is +ve small then i is –ve smallif θ is +ve small and dθ/dt is Zero then i is –ve smallif θ +ve small and dθ
/dt is +ve small then i is -ve medium
-
ε
+
ε
ε
2
-
ε
2
-
ε
3
ε
3
+ve small
-ve small
1
Quantity (
θ
,
θ
.
, i)
zero
Rules InvolvedSlide32
Suppose θ is 1 radian and dθ/dt is 1 rad/sec
μ
zero(θ =1)=0.8 (say)Μ+ve-small(θ =1)=0.4 (say)μzero(dθ/dt =1)=0.3 (say)
μ+ve-small(dθ/dt =1)=0.7 (say)
-
ε
+
ε
ε
2
-
ε
2
-
ε
3
ε
3
+ve small
-ve small
1
Quantity (
θ
,
θ
.
, i)
zero
Fuzzification
1rad
1 rad/secSlide33
Suppose θ is 1 radian and dθ/dt is 1 rad/sec
μ
zero(θ =1)=0.8 (say)μ +ve-small(θ =1)=0.4 (say)μzero(dθ/dt =1)=0.3 (say)
μ+ve-small(dθ/dt =1)=0.7 (say)
Fuzzification
if
θ
is Zero and dθ/dt is Zero then i is Zero
min(0.8, 0.3)=0.3
hence μzero
(i)=0.3if θ is Zero and dθ/dt is +ve small then i is –ve small
min(0.8, 0.7)=0.7 hence
μ-ve-small(i)=0.7
if θ is +ve small and dθ/dt is Zero then i is –ve small
min(0.4, 0.3)=0.3 hence
μ-ve-small(i)=0.3if θ +ve small and dθ/dt is +ve small then i is -ve medium
min(0.4, 0.7)=0.4 hence
μ-ve-medium
(i)=0.4Slide34
-
ε
+
ε
-
ε
2
-
ε
3
-ve small
1
zero
Finding
i
0.4
0.3
Possible candidates:
i=0.5 and -0.5 from the “zero” profile and
μ
=0.3
i=-0.1 and -2.5 from the “-ve-small” profile and
μ
=0.3
i=-1.7 and -4.1 from the “-ve-small” profile and
μ
=0.3
-4.1
-2.5
-ve small
-ve medium
0.7Slide35
-
ε
+
ε
-ve small
zero
Defuzzification: Finding
i
by the
centroid
method
Possible candidates:
i is the x-coord of the centroid of the areas given by the
blue trapezium
, the
green trapeziums
and the black trapezium
-4.1
-2.5
-ve medium
Required i value
Centroid of three
trapezoids