Kuliah 12 Logika Fuzzy dasar dasar sekali

fuzzzy

1. History, State of the Art, and Future Development
1965Seminal Paper “Fuzzy Logic” by Prof. LotfiZadeh,
Faculty in Electrical Engineering, U.C. Berkeley, Sets the
Foundation of the “Fuzzy Set Theory”
1970First Application of Fuzzy Logic in Control Engineering
(Europe)
1975Introduction of Fuzzy Logic in Japan
1980Empirical Verification of Fuzzy Logic in Europe
1985Broad Application of Fuzzy Logic in Japan
1990Broad Application of Fuzzy Logic in Europe
1995Broad Application of Fuzzy Logic in the U.S.
2000Fuzzy Logic Becomes a Standard Technology and Is Also
Applied in Data and Sensor Signal Analysis. Application of
Fuzzy Logic in Business and Finance.
Today, Fuzzy Logic Has
Already Become the
Standard Technique for
Multi-Variable Control !

2. 26
What is a fuzzy rule?
A fuzzy rule can be defined as a conditional
statement in the form:
IFxisA
THEN yis B
where xand yare linguistic variables; and Aand B
are linguistic values determined by fuzzy sets on the
universe of discourses Xand Y, respectively.

3. What is the difference between classical and
fuzzy rules?
A classical IF-THEN rule uses binary logic, for
example,
Rule: 1
IFspeedis>100
THENstopping_distanceislong
Rule: 2
IFspeedis<40
THENstopping_distanceisshort

4. 28
We can also represent the stopping distance rules in a
fuzzy form:
Rule:1
IFspeedisfast
THEN stopping_distanceis long
Rule: 2
IFspeedisslow
THEN stopping_distance is short
WHAT IS THE DIFFERENCE BETWEEN CLASSICAL AND FUZZY RULES?
In a fuzzy system, all rules fire to some extent, or in
other words they fire partially. If the antecedent is true
to some degree of membership, then the consequent is
also true to that same degree.

5. 29
FUZZY RULES
•In fuzzy rules, the linguistic variable speedalso has the range (the
universe of discourse) between 0 and 220 km/h, but this range includes
fuzzy sets, such as slow, mediumand fast.
•The universe of discourse of the linguistic variable stopping_distancecan
be between 0 and 300 m and may include such fuzzy sets as short,
mediumand long.

6. Tall men Heavy men
180
Degree of
Membership
1.0
0.0
0.2
0.4
0.6
0.8
Height, cm
190 200 70 80 100160
Weight, kg
120
Degree of
Membership
1.0
0.0
0.2
0.4
0.6
0.8
Fuzzy sets of talland heavymen
These fuzzy sets provide the basis for a weight estimation model.
The model is based on a relationship between a man’s height and
his weight:
IFheightistall
THENweightisheavy

7. 31
The value of the output or a truth membership grade
of the rule consequent can be estimated directly from a
corresponding truth membership grade in the
antecedent. This form of fuzzy inference uses a
method called monotonic selection.
Tall men
Heavy men
180
Degree of
Membership
1.0
0.0
0.2
0.4
0.6
0.8
Height, cm
190 200 70 80 100160
Weight, kg
120
Degree of
Membership
1.0
0.0
0.2
0.4
0.6
0.8

8. 32
A fuzzy rule can have multiple antecedents, for
example:
IFproject_durationislong
ANDproject_staffingislarge
ANDproject_fundingisinadequate
THENriskishigh
IFserviceisexcellent
ORfoodisdelicious
THENtipisgenerous