Matakuliah
Tahun
Versi
: H0434/Jaringan Syaraf Tiruan
: 2005
:1
Pertemuan 22
FUZZIFIKASI DAN DEFUZZIFIKASI
1
Learning Outcomes
Pada akhir pertemuan ini, diharapkan mahasiswa
akan mampu :
• Menjelaskan FUZZY to CRISP conversion
dan sebaliknya.
2
Outline Materi
• Proses Fuzzifikasi.
• Proses Defuzzifikasi.
3
FUZZIFIKASI &
DEFUZZIFIKASI

Fuzzification


Inference Mechanism



Scales and maps input variables to fuzzy
sets
Approximate reasoning
Deduces the control action
Defuzzification

Convert fuzzy output values to control
signals
4
FUZZY CONTROLLER
Fuzzy Controller
Rule
Base
Pre
processing
Fuzzification
Defuzzification
Post
Processing
Inference
engine
5
FUZZY CONTROLLER
action
Defuzzification
Module
Fuzzy
Inference
Engine
Controlled
Process
condition
Fuzzy Controller
Fuzzy
Rule base
Fuzzification
Module
6
FUZZIFICATION
Transformation from crisp input to fuzzy
input.
1
0.6
input
7
APPROXIMATE REASONING
y = f(x)
Then we can make
inferences easily
premise y = f(x)
fact x = x’
-----------------------------consequence y = f( x’ )
8
BASIC INFERENCES
x is A
AB
-----------x is B
Mary is very young
very young  young
------------------------Mary is young
x is A
x is B
--------------x is A B
and
pressure is not very high
pressure is not very low
-----------------------------------pressure is not very high
not very low
9
MAMDANI’S IMPLICATION
OPERATOR
if x is A then y is B
x is A’
----------------------y is B’
10
INFERENCE
Penarikan kesimpulan dari semua fungsi keanggotaan
yang sudah didefinisikan menggunakan RULE BASE.
IF A AND B THEN C
RULE 1 : A1 , B1  C1
RULE 2 : A2 , B2  C2
FAKTA : x , Dx
---------------------------------------KESIMPULAN :
C’
11
MIN-MAX METHOD
NL
PM
A1
PL
B1
NM
A2
C1
PS
C1'
PM
B2
C2
C2'
x
Dx
C' = C1' U C2'
METODE MIN-MAX
12
DEFUZZIFICATION
 Transformation from fuzzy output to crisp ouput.
 Defuzzification is a process to get a non-fuzzy
value that best represents the possibility
distribution of an inferred fuzzy control action.
 There is no systematic procedure for choosing a
good defuzzification strategy.
 Selection of defuzzification procedure depends on
the properties of the application.
13
CENTROID OF THE AREA
14
EXAMPLE
15
MEAN OF MAXIMUM
16
EXAMPLE
17
HEIGHT METHOD
18
WEIGHTED AVERAGE
19
EXAMPLE
20
BISECTOR OF THE AREA
21
FIRST/LAST MAXIMA
22
EXAMPLE
23