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I
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8
8
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8708
I
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,
Vo
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8
,
No
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5
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Octo
b
er
2
0
1
8
:
3
6
5
7
–
3
6
6
5
3658
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-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
6
5
7
–
3
6
6
5
3660
3.
ADAP
T
I
VE
N
E
URO
-
F
U
Z
Z
Y
I
NF
E
R
E
NC
E
CO
NT
RO
L
(
ANF
I
S)
A
N
FIS
is
a
m
u
lt
i
-
la
y
er
f
ee
d
f
o
r
w
ar
d
n
et
w
o
r
k
[
2
0
]
w
i
th
s
u
p
er
v
is
ed
lear
n
i
n
g
ca
p
ab
ilit
y
,
as
s
h
o
w
n
i
n
Fig
u
r
e
3
.
I
t
h
as
t
w
o
o
p
ti
m
izat
i
o
n
tech
n
iq
u
e
o
p
tio
n
s
f
o
r
its
le
ar
n
in
g
m
e
th
o
d
.
I
t
ca
n
eith
er
u
s
e
a
h
y
b
r
id
m
eth
o
d
,
w
h
ic
h
co
n
s
is
t
s
o
f
b
ac
k
-
p
r
o
p
ag
atio
n
a
n
d
leas
t
s
q
u
ar
e
e
s
ti
m
atio
n
,
o
r
a
b
ac
k
-
p
r
o
p
ag
atio
n
m
et
h
o
d
.
T
h
e
A
NFI
S
m
o
d
el
is
b
ased
o
n
t
h
e
T
ak
ag
i
-
Su
g
en
o
(
T
-
S)
f
u
zz
y
m
o
d
el.
T
h
er
ef
o
r
e,
its
o
u
tp
u
t
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
w
ill
b
e
eith
er
a
co
n
s
ta
n
t o
r
a
lin
ea
r
.
I
t w
il
l a
ls
o
f
o
llo
w
t
h
e
f
ir
s
t o
r
d
er
T
-
S
m
o
d
el
r
u
le
w
h
ic
h
is
:
I
f
I
n
p
u
t 1
=
x
a
n
d
I
n
p
u
t 2
=
y
,
th
en
Ou
tp
u
t is
z
=
Ax
+
B
y
+
C
(
1
0
)
w
h
er
e
A
,
B
,
an
d
C
ar
e
co
n
s
tan
t.
W
h
en
th
e
o
u
tp
u
t i
s
co
n
s
ta
n
t,
it
m
ea
n
s
t
h
at
th
e
v
al
u
es o
f
A
an
d
B
ar
e
ze
r
o
.
A
N
FIS
also
u
s
e
s
th
e
s
a
m
e
p
r
in
cip
le
as
S
u
g
en
o
FI
S
to
d
eter
m
i
n
e
th
e
n
u
m
b
er
o
f
r
u
les.
T
h
e
n
u
m
b
er
o
f
r
u
les
d
ep
en
d
s
o
n
t
h
e
n
u
m
b
er
o
f
in
p
u
ts
a
n
d
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
u
s
ed
f
o
r
ea
ch
i
n
p
u
t.
T
h
e
n
u
m
b
er
o
f
r
u
l
e
s
f
o
llo
w
s
t
h
e
f
o
llo
w
i
n
g
eq
u
atio
n
T
h
e
n
u
m
b
er
o
f
A
N
FIS
r
u
le
=
(
1
1
)
w
h
er
e
µ
is
t
h
e
n
u
m
b
er
o
f
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
w
h
ile
u
is
th
e
n
u
m
b
er
o
f
i
n
p
u
ts
.
Sin
ce
t
h
er
e
ar
e
f
o
u
r
in
p
u
t
s
(
th
e
er
r
o
r
o
f
an
g
le,
th
e
er
r
o
r
o
f
d
is
p
lace
m
e
n
t
an
d
th
eir
r
at
es)
in
th
e
co
n
tr
o
ller
,
th
e
n
u
m
b
er
o
f
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
w
ill
o
n
l
y
b
e
t
w
o
s
tates
(
Neg
at
iv
e
an
d
P
o
s
itiv
e)
to
av
o
id
r
u
le
ex
p
lo
s
io
n
.
T
h
er
ef
o
r
e,
t
h
e
n
u
m
b
er
o
f
r
u
le
s
i
s
4
2
=
1
6
r
u
le
s
.
Ge
n
er
alize
d
b
ell
-
s
h
ap
ed
m
e
m
b
er
s
h
ip
is
ch
o
s
e
n
s
in
ce
it p
r
o
v
id
es a
s
m
o
o
th
er
r
e
s
p
o
n
s
e
f
o
r
n
o
n
-
lin
ea
r
s
y
s
te
m
s
.
T
o
tu
n
e
t
h
e
ANFI
S
co
n
tr
o
ller
,
an
in
p
u
t
-
o
u
tp
u
t
d
ata
is
n
ee
d
e
d
,
w
h
er
e
t
h
is
d
ata
p
r
o
v
id
es
t
h
e
r
eq
u
ir
ed
r
elatio
n
s
h
ip
b
et
w
ee
n
th
e
ac
ce
ler
atio
n
o
f
t
h
e
ca
r
t
a
n
d
th
e
in
p
u
ts
.
T
h
e
in
p
u
t
-
o
u
tp
u
t
d
ata
was
co
llected
f
r
o
m
a
clo
s
ed
lo
o
p
s
y
s
te
m
u
s
in
g
a
n
L
QR
co
n
tr
o
ller
.
C
o
llecti
n
g
t
h
e
d
ata
is
co
n
s
id
er
ed
as t
h
e
f
i
r
s
t
an
d
th
e
m
o
s
t c
r
u
cial
s
tep
in
t
h
e
ad
ap
tiv
e
n
e
u
r
o
-
f
u
zz
y
co
n
tr
o
ller
s
in
ce
in
co
m
p
lete
o
r
w
r
o
n
g
d
ata
w
ill
le
ad
to
u
n
ac
ce
p
tab
le
p
er
f
o
r
m
a
n
ce
o
r
u
n
s
tab
le
s
y
s
te
m
.
T
h
er
ef
o
r
e,
in
th
is
p
r
o
j
ec
t,
th
e
in
p
u
t
-
o
u
tp
u
t
d
ata
w
er
e
co
llected
in
d
if
f
er
en
t
s
itu
a
tio
n
s
w
h
er
e
t
h
e
in
i
tial p
o
s
itio
n
o
f
t
h
e
ca
r
t a
n
d
p
en
d
u
l
u
m
w
er
e
d
if
f
er
en
t i
n
ea
c
h
s
it
u
atio
n
.
Fig
u
r
e
3
.
Stru
ct
u
r
e
o
f
A
NFI
S
co
n
tr
o
ller
B
ased
o
n
th
e
co
llected
d
ata,
th
e
o
p
ti
m
iza
tio
n
lear
n
in
g
m
et
h
o
d
ch
o
s
en
w
a
s
t
h
e
h
y
b
r
id
alg
o
r
ith
m
,
w
h
ile
t
h
e
v
alu
e
o
f
ac
ce
p
tab
le
er
r
o
r
a
n
d
n
u
m
b
er
o
f
ep
o
ch
s
w
a
s
s
elec
ted
to
b
e
0
.
0
0
0
1
an
d
1
0
0
,
r
esp
ec
tiv
el
y
.
W
h
en
t
h
e
s
tr
u
c
tu
r
e
o
f
t
h
e
d
es
i
r
ed
n
eu
r
o
-
f
u
zz
y
w
as
cr
ea
ted
,
th
e
co
n
tr
o
ller
s
tar
ted
its
tr
ai
n
wh
ich
s
to
p
p
ed
w
h
e
n
th
e
er
r
o
r
r
ea
ch
ed
th
e
to
ler
an
ce
er
r
o
r
o
r
th
e
m
a
x
i
m
u
m
n
u
m
b
e
r
o
f
ep
o
ch
s
.
4.
RE
S
U
L
T
S
A
ND
AN
AL
Y
SI
S
T
h
e
A
NFI
S
co
n
tr
o
ller
w
a
s
i
m
p
le
m
en
ted
i
n
M
A
T
L
A
B
u
s
i
n
g
A
NFI
S
ed
ito
r
.
I
n
S
i
m
u
lin
k
,
t
h
e
s
a
m
p
li
n
g
ti
m
e
w
as set to
b
e
0
.
0
1
s
in
th
e
s
i
m
u
latio
n
w
h
i
le
it
w
a
s
0
.
0
0
5
s
in
th
e
ex
p
er
i
m
e
n
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8708
A
d
a
p
tive
N
eu
r
o
-
F
u
z
z
y
C
o
n
tr
o
l A
p
p
r
o
a
ch
f
o
r
a
S
in
g
le
I
n
ve
r
t
ed
P
en
d
u
lu
m
…
(
Mo
h
a
mme
d
A
.
A
.
A
l
-
Mekh
la
fi
)
3661
4
.
1
.
Si
m
ula
t
io
n r
es
ults
Fig
u
r
e
4
s
h
o
w
s
t
h
e
s
tr
u
ct
u
r
e
o
f
t
h
e
i
n
v
er
ted
p
en
d
u
lu
m
s
y
s
te
m
i
n
Si
m
u
li
n
k
.
T
h
e
ca
r
t
w
a
s
s
et
to
tr
ac
k
th
e
i
n
p
u
t
w
h
e
n
it
ch
a
n
g
ed
f
r
o
m
0
to
0
.
2
m
a
t
t
=
1
s
.
A
t
th
e
s
a
m
e
ti
m
e,
th
e
p
en
d
u
lu
m
s
h
o
u
ld
r
e
m
a
in
n
ea
r
th
e
u
p
r
ig
h
t p
o
s
itio
n
w
it
h
i
n
t
h
e
all
o
w
ab
le
r
an
g
e.
T
h
e
in
itial c
o
n
d
itio
n
s
o
f
t
h
e
p
en
d
u
lu
m
a
n
d
ca
r
t
w
er
e
s
et
to
b
e
0
.
Fig
u
r
e
4
Sch
e
m
atic
d
iag
r
a
m
u
s
in
g
A
NFI
S
co
n
tr
o
ller
Fig
u
r
e
5
p
r
esen
t
s
t
h
e
r
esp
o
n
s
e
o
f
th
e
ca
r
t
a
n
d
th
e
p
e
n
d
u
l
u
m
i
n
th
e
s
i
m
u
latio
n
.
S
in
ce
n
o
in
p
u
t
w
a
s
ap
p
lied
at
th
e
b
eg
in
n
i
n
g
an
d
th
e
i
n
itial
co
n
d
itio
n
o
f
th
e
p
lan
t
w
as
s
et
to
0
,
th
e
r
esp
o
n
s
e
o
f
th
e
i
n
v
er
ted
p
en
d
u
lu
m
s
y
s
te
m
s
tar
ted
w
h
e
n
t
h
e
i
n
p
u
t
c
h
a
n
g
ed
f
r
o
m
0
to
0
.
2
m
at
t=1
s
.
I
f
th
e
ca
r
t
w
er
e
to
m
o
v
e
s
u
d
d
en
l
y
to
th
e
p
o
s
itiv
e
d
ir
ec
tio
n
,
th
e
p
en
d
u
l
u
m
w
o
u
ld
f
all
to
th
e
n
e
g
ativ
e
d
ir
ec
tio
n
s
i
n
ce
th
e
ca
r
t
an
d
p
en
d
u
lu
m
w
er
e
co
u
p
led
to
ea
ch
o
th
er
[
9
]
.
T
h
er
ef
o
r
e,
w
h
en
t
h
e
ca
r
t
w
as
s
et
t
o
th
e
p
o
s
iti
v
e
d
ir
ec
tio
n
,
i
t
w
o
u
ld
f
ir
s
t
m
o
v
e
to
t
h
e
o
p
p
o
s
ite
d
ir
ec
tio
n
in
o
r
d
er
t
o
p
r
ev
en
t t
h
e
p
en
d
u
l
u
m
f
r
o
m
f
al
lin
g
b
ef
o
r
e
tr
ac
k
i
n
g
t
h
e
d
esire
d
p
o
s
itio
n
.
(
a)
(
b
)
Fig
u
r
e
5
.
T
h
e
r
esp
o
n
s
e
f
o
r
A
N
FIS
co
n
tr
o
ller
in
s
i
m
u
latio
n
;
(
a)
C
ar
t’
s
R
esp
o
n
s
e,
(
b
)
A
n
g
le
’
s
R
esp
o
n
s
e
Fro
m
Fig
u
r
es
5
(
a)
an
d
Fig
u
r
e
5
(
b
)
,
it
ca
n
b
e
s
ee
n
th
at
th
e
ca
r
t
n
ee
d
ed
1
.
5
s
to
r
ea
ch
t
h
e
d
esire
d
lo
ca
tio
n
,
w
h
i
le
th
e
a
n
g
le
n
ee
d
ed
2
.
2
s
to
r
etu
r
n
b
ac
k
to
its
u
p
r
ig
h
t
p
o
s
itio
n
,
w
it
h
n
o
s
tead
y
-
s
tate
er
r
o
r
in
b
o
t
h
r
esp
o
n
s
es.
T
h
e
r
esp
o
n
s
e
o
f
th
e
ca
r
t
w
as
v
er
y
s
m
o
o
th
w
it
h
n
o
o
v
er
s
h
o
o
t.
Ho
w
ev
er
,
t
h
e
o
v
er
s
h
o
o
t
o
f
th
e
a
n
g
l
e
w
a
s
0
.
0
9
5
r
a
d
w
h
e
n
in
p
u
t
w
as
ap
p
lied
.
4
.
2
.
E
x
peri
m
e
nt
r
esu
lt
s
T
h
e
in
v
er
ted
p
en
d
u
lu
m
ap
p
ar
atu
s
m
e
n
tio
n
ed
in
s
ec
tio
n
2
.
1
w
as
u
s
ed
.
A
t
th
e
b
eg
i
n
n
i
n
g
,
th
e
p
en
d
u
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g
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I
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2
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Fig
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r
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u
r
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e
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e
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2
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m
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[
4
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.
Si
m
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e
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N
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.
1
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ula
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h
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g
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(
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ig
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7
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b
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r
t
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m
p
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in
M
A
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ep
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atel
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h
t
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m
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n
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.
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h
e
s
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lid
lin
e
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n
tr
o
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,
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esp
ec
tiv
el
y
.
(
a)
(
b
)
Fig
u
r
e
7
.
C
ar
t r
esp
o
n
s
e
f
o
r
b
o
th
co
n
tr
o
ller
s
i
n
s
i
m
u
latio
n
;
(
a)
C
ar
t’
s
R
esp
o
n
s
e,
(
b
)
A
n
g
le
’
s
R
esp
o
n
s
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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&
C
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p
E
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I
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N:
2088
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8708
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3663
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m
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h
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d
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f
t
h
e
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N
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d
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g
e
n
o
FIS
co
n
tr
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ller
s
in
t
h
e
s
i
m
u
lati
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n
.
T
ab
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3
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m
m
ar
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h
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R
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s
p
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s
es
f
o
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th
e
A
NFI
S a
n
d
S
u
g
e
n
o
FI
S
C
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n
tr
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ller
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in
t
h
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m
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[
4
]
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m
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latio
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u
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h
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a
m
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m
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l
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n
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et
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n
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g
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n
d
th
e
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n
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ller
in
t
h
e
ex
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m
en
t.
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ts
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m
p
ar
ativ
e
p
er
f
o
r
m
an
ce
is
s
h
o
w
n
in
T
ab
le
4
.
T
ab
le
4.
C
o
m
p
ar
is
o
n
b
et
w
ee
n
A
N
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an
d
S
u
g
en
o
FIS
co
n
tr
o
ller
s
in
ex
p
er
i
m
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t
C
a
r
t
’
s
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e
sp
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se
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n
g
l
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sp
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a
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n
o
F
I
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e
t
t
l
i
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g
t
i
me
1
.
5
s
4
s
1
.
7
s
3
.
7
s
O
v
e
r
sh
o
o
t
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o
o
v
e
r
sh
o
o
t
8%
0
.
0
8
r
a
d
0
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0
0
5
r
a
d
S
t
e
a
d
y
-
s
t
a
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e
r
r
o
r
1
.
5
%
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5
%
0
.
0
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a
d
0
.
0
5
r
a
d
L
i
k
e
th
e
s
i
m
u
latio
n
,
t
h
e
ANFI
S
co
n
tr
o
ller
g
av
e
b
etter
p
er
f
o
r
m
a
n
ce
an
d
s
m
o
o
th
er
r
esp
o
n
s
e
co
m
p
ar
ed
to
th
e
Su
g
e
n
o
FIS
co
n
tr
o
ller
.
E
v
en
th
o
u
g
h
th
e
Su
g
en
o
FIS
co
n
tr
o
ller
w
as
a
b
le
to
s
tab
ilize
th
e
s
y
s
te
m
,
th
er
e
w
a
s
a
h
u
g
e
s
te
ad
y
-
s
tate
er
r
o
r
lev
el
f
o
r
t
h
e
c
ar
t’
s
r
esp
o
n
s
e
d
u
r
i
n
g
t
h
e
e
x
p
er
i
m
en
t.
T
h
is
er
r
o
r
m
a
y
ca
u
s
e
s
o
m
e
p
r
o
b
lem
s
an
d
d
if
f
ic
u
lt
ies
to
th
e
en
d
u
s
e
r
.
T
h
e
Su
g
e
n
o
Fu
zz
y
co
n
tr
o
l
ler
also
h
ad
a
h
ig
h
s
ettli
n
g
ti
m
e
co
m
p
ar
ed
to
t
h
e
A
N
FIS
co
n
tr
o
ller
as
it
n
ee
d
ed
ap
p
r
o
x
i
m
atel
y
4
s
to
r
ea
ch
s
t
ab
ilit
y
.
O
v
er
all,
it
w
a
s
clea
r
th
at
u
s
i
n
g
th
e
A
N
FI
S
m
et
h
o
d
w
a
s
m
u
c
h
b
etter
in
t
er
m
s
o
f
s
ettli
n
g
t
i
m
e
a
n
d
s
tead
y
-
s
tate
er
r
o
r
.
6.
CO
NCLU
SI
O
N
T
h
e
o
b
j
ec
tiv
e
o
f
t
h
is
s
t
u
d
y
w
a
s
ac
h
iev
ed
b
y
d
esi
g
n
i
n
g
a
n
ad
ap
tiv
e
n
e
u
r
o
-
f
u
zz
y
i
n
f
er
en
ce
s
y
s
te
m
(
A
NFI
S)
to
co
n
tr
o
l
th
e
in
v
er
ted
p
en
d
u
lu
m
s
y
s
te
m
w
it
h
i
n
a
b
r
ief
ti
m
e.
T
h
e
co
n
tr
o
ller
w
a
s
i
m
p
le
m
e
n
ted
i
n
th
e
MA
T
L
A
B
e
n
v
ir
o
n
m
en
t
u
s
i
n
g
t
h
e
A
N
FIS
ed
ito
r
.
T
h
e
ANFI
S
co
n
tr
o
ller
p
r
o
v
id
ed
an
ef
f
icie
n
t
a
n
d
q
u
ic
k
r
esp
o
n
s
e
w
it
h
a
s
m
all
er
r
o
r
in
b
o
th
s
i
m
u
la
tio
n
a
n
d
ex
p
er
i
m
e
n
t.
I
t
also
s
h
o
w
ed
b
etter
p
er
f
o
r
m
an
ce
i
n
ter
m
s
o
f
o
v
er
s
h
o
o
t,
s
tab
ilit
y
a
n
d
s
et
tli
n
g
ti
m
e
co
m
p
ar
ed
to
Su
g
e
n
o
F
I
S.
Ho
w
e
v
er
,
t
h
e
A
NFI
S
co
n
t
r
o
ller
p
er
f
o
r
m
a
n
ce
d
ep
en
d
ed
h
ea
v
il
y
o
n
th
e
in
p
u
t
-
o
u
tp
u
t
d
ata
co
llected
f
r
o
m
a
clo
s
ed
lo
o
p
co
n
tr
o
lled
s
y
s
t
e
m
.
T
h
er
ef
o
r
e,
t
h
is
s
tep
s
h
o
u
ld
b
e
i
m
p
le
m
e
n
ted
o
n
l
y
a
f
ter
i
n
-
d
ep
t
h
a
n
d
ca
r
ef
u
l
p
lan
n
i
n
g
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
r
esear
ch
w
as
s
u
p
p
o
r
ted
b
y
t
h
e
Mi
n
is
tr
y
o
f
Hi
g
h
er
E
d
u
ca
tio
n
o
f
Ma
la
y
s
ia
an
d
b
y
th
e
R
esear
ch
Un
i
v
er
s
it
y
Gr
a
n
t
(
UT
M
-
GUP
)
o
f
U
n
iv
er
s
iti
T
ek
n
o
lo
g
i
Ma
la
y
s
ia
(
w
i
t
h
Gr
a
n
t
No
.
1
1
H4
9
)
.
T
h
e
au
t
h
o
r
s
ar
e
g
r
atef
u
l
f
o
r
th
eir
s
u
p
p
o
r
t a
n
d
m
ak
in
g
t
h
is
r
esear
c
h
v
iab
le.
RE
F
E
R
E
NC
E
S
[1
]
L
u
n
d
b
e
rg
KH
,
Ba
rto
n
T
W
,
“
Histo
ry
o
f
In
v
e
rted
-
p
e
n
d
u
l
u
m
S
y
st
e
m
s”
,
IFA
C
P
r
o
c
e
e
d
in
g
s
,
v
o
l.
42
,
n
o
.
2
4
,
p
p
.
1
3
1
-
1
3
5
,
Ja
n
2
0
1
0
.
[2
]
Yu
su
f
LA
a
n
d
M
a
g
a
ji
N,
“
GA
-
P
ID
Co
n
tr
o
ll
e
r
f
o
r
P
o
siti
o
n
C
o
n
tr
o
l
o
f
In
v
e
rted
P
e
n
d
u
l
u
m
”
,
IEE
E
6
t
h
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
a
p
ti
v
e
S
c
ien
c
e
&
T
e
c
h
n
o
lo
g
y
(
ICAS
T
)
,
p
p
.
1
-
5
,
2
0
1
4
.
[3
]
Bu
g
e
ja
M
,
“
No
n
-
li
n
e
a
r
S
w
in
g
-
u
p
a
n
d
S
tab
il
izi
n
g
C
o
n
tro
l
o
f
a
n
In
v
e
rted
P
e
n
d
u
lu
m
S
y
s
t
e
m
”
,
T
h
e
IEE
E
Reg
i
o
n
8
EUROCON 2
0
0
3
,
Co
mp
u
ter
a
s
a
T
o
o
l
.
2
0
0
3
;
v
o
l
.
2
,
p
p
.
4
3
7
-
4
4
1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
6
5
7
–
3
6
6
5
3664
[4
]
No
u
r
M
I,
O
o
i
J,
Ch
a
n
KY
,
“
F
u
z
z
y
L
o
g
ic
Co
n
tro
l
v
s.
Co
n
v
e
n
ti
o
n
a
l
P
ID
C
o
n
tr
o
l
o
f
a
n
In
v
e
rted
P
e
n
d
u
lu
m
Ro
b
o
t”,
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
telli
g
e
n
t
a
n
d
A
d
v
a
n
c
e
d
S
y
ste
ms
(
ICIAS
)
,
Ku
a
la
L
u
m
p
u
r,
M
a
la
y
sia
,
2
0
0
7
,
p
p
.
2
0
9
-
214
.
[5
]
T
a
t
ik
o
n
d
a
RC,
Ba
tt
u
la
VP
,
Ku
m
a
r
V
,
“
Co
n
tro
l
o
f
In
v
e
rted
P
e
n
d
u
lu
m
u
sin
g
A
d
a
p
ti
v
e
N
e
u
ro
F
u
z
z
y
In
f
e
r
e
n
c
e
S
tru
c
tu
re
(A
NFIS
)”
,
Pro
c
e
e
d
in
g
s
o
f
2
0
1
0
IEE
E
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
siu
m
o
n
Circ
u
it
s
a
n
d
S
y
ste
ms
,
P
a
ris
,
20
10,
p
p
.
1
3
4
8
-
1
3
5
1
.
[6
]
S
in
g
h
Y,
Bh
a
t
o
ti
a
M
,
M
i
tra
R,
“
H
y
b
rid
Co
n
tro
ll
e
r
f
o
r
In
v
e
rted
P
e
n
d
u
lu
m
”
,
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
s
in
P
o
we
r Co
n
v
e
rs
io
n
a
n
d
En
e
rg
y
T
e
c
h
n
o
l
o
g
ies
(
AP
CET
)
,
M
y
lav
a
r
a
m
,
In
d
ia
,
2
0
1
2
,
pp
1
-
4.
[7
]
Li
J,
G
a
o
X
,
H
u
a
n
g
Q,
M
a
tsu
m
o
to
O,
“
Co
n
tro
ll
e
r
D
e
sig
n
o
f
a
t
w
o
-
wh
e
e
led
In
v
e
rted
P
e
n
d
u
l
u
m
M
o
b
i
le
Ro
b
o
t”
,
IEE
E
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
M
e
c
h
a
tro
n
ics
a
n
d
A
u
t
o
ma
ti
o
n
(
ICM
A
)
,
T
a
k
a
m
a
tsu
,
Ja
p
a
n
,
2
0
0
8
,
p
p
.
7
-
12
.
[8
]
Jia
-
Ju
n
W
,
“
P
o
siti
o
n
a
n
d
S
p
e
e
d
T
ra
c
k
in
g
Co
n
tr
o
l
o
f
In
v
e
rted
P
e
n
d
u
lu
m
b
a
se
d
o
n
d
o
u
b
le
P
ID
C
o
n
tro
ll
e
rs”
,
3
4
t
h
Ch
in
e
s
e
Co
n
tro
l
C
o
n
fer
e
n
c
e
(
CC
C)
,
Ha
n
g
z
h
o
u
,
Ch
i
n
a
.
2
0
1
5
3
4
t
h
Ch
in
e
se
2
0
1
5
,
p
p
.
4
1
9
7
-
4
2
0
1
.
[9
]
Yo
n
g
-
c
h
u
a
n
T
,
F
e
n
g
L
,
Qia
n
Q,
Ya
n
g
Y,
“
S
tab
il
izin
g
P
lan
a
r
I
n
v
e
rted
P
e
n
d
u
l
u
m
S
y
ste
m
b
a
s
e
d
o
n
F
u
z
z
y
n
in
e
-
p
o
in
t
Co
n
tr
o
ll
e
r”
,
I
n
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ie
n
c
e
(
IJ
EE
CS
)
,
v
o
l.
1
2
,
n
o
.
1
,
p
p
.
4
2
2
-
4
3
2
,
2
0
1
4
.
[1
0
]
T
rip
a
th
i
S
K,
P
a
n
d
a
y
H,
G
a
u
r
P
,
“
Ro
b
u
st
C
o
n
tro
l
o
f
In
v
e
rted
P
e
n
d
u
l
u
m
u
sin
g
F
u
z
z
y
L
o
g
ic
Co
n
tro
ll
e
r
”
,
S
tu
d
e
n
ts
Co
n
fer
e
n
c
e
o
n
En
g
i
n
e
e
rin
g
a
n
d
S
y
ste
ms
(
S
CES
)
,
A
ll
a
h
a
b
a
d
,
I
n
d
ia
.
2
0
1
3
,
p
p
.
1
-
6
.
[1
1
]
Al
-
M
e
k
h
a
l
f
i
M
A
,
W
a
h
id
H
,
“
M
o
d
e
ll
in
g
a
n
d
Co
n
tr
o
l
o
f
a
No
n
-
li
n
e
a
r
In
v
e
rted
P
e
n
d
u
l
u
m
u
sin
g
a
n
Ad
a
p
ti
v
e
Ne
u
ro
-
F
u
z
z
y
Co
n
tro
ll
e
r
”
,
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
f
Relia
b
le
In
fo
rm
a
ti
o
n
a
n
d
Co
mm
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
y
(
IRI
CT
),
S
p
rin
g
e
r
,
Ch
a
m
.
2
0
1
7
,
p
p
.
2
1
8
-
2
2
5
.
,
[1
2
]
Ha
n
a
fy
T
O,
M
e
t
w
a
ll
y
MK
,
“
S
i
m
p
li
f
ica
ti
o
n
s
o
f
th
e
Ru
le
b
a
se
f
o
r
t
h
e
sta
b
il
iza
t
io
n
o
f
In
v
e
rted
P
e
n
d
u
lu
m
S
y
ste
m
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(
I
J
EE
CS
)
.
2
0
1
4
;
1
2
(
7
):
pp
5
2
2
5
-
3
4
.
[1
3
]
Da
stra
n
j
M
R,
M
o
g
h
a
d
d
a
s
M
,
Afg
h
u
S
S
,
R
o
u
h
a
n
i
M
,
“
P
ID
C
o
n
tr
o
l
o
f
In
v
e
rted
P
e
n
d
u
l
u
m
u
sin
g
P
a
rti
c
le
S
wa
r
m
O
p
ti
m
iza
ti
o
n
(
P
S
O)
A
lg
o
rit
h
m
”
,
IEE
E
3
rd
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Co
mm
u
n
ica
ti
o
n
S
o
ft
w
a
re
a
n
d
Ne
tw
o
rk
s
,
2
0
1
1
,
p
p
.
5
7
5
-
5
7
8
.
[1
4
]
P
ra
sa
d
L
B,
G
u
p
ta
HO
,
T
y
a
g
i
B
,
“
In
telli
g
e
n
t
C
o
n
tr
o
l
o
f
No
n
li
n
e
a
r
In
v
e
rted
P
e
n
d
u
lu
m
Dy
n
a
m
i
c
a
l
S
y
ste
m
w
it
h
Distu
rb
a
n
c
e
In
p
u
t
u
si
n
g
F
u
z
z
y
Lo
g
ic
S
y
ste
m
s
”
,
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Rec
e
n
t
Ad
v
a
n
c
e
me
n
ts
in
El
e
c
trica
l,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
E
n
g
in
e
e
rin
g
,
S
iv
a
k
a
si,
In
d
ia
,
2
0
1
1
,
p
p
.
1
3
6
-
1
4
1
.
[1
5
]
S
h
il
l
P
C
,
A
k
h
a
n
d
M
A
,
M
u
ra
se
K
,
“
F
u
z
z
y
L
o
g
ic
Co
n
tro
ll
e
r
f
o
r
a
n
In
v
e
rted
P
e
n
d
u
lu
m
S
y
ste
m
u
sin
g
Qu
a
n
tu
m
G
e
n
e
ti
c
Op
t
im
iz
a
ti
o
n
”
,
1
4
t
h
In
te
rn
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
m
p
u
t
e
r
a
n
d
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
(ICCIT
),
Dh
a
k
a
,
Ba
n
g
lad
e
sh
,
2
0
1
1
,
p
p
.
5
0
3
-
5
0
8
.
[1
6
]
S
a
id
i
K,
A
ll
a
d
M
,
“
F
u
z
z
y
Co
n
t
ro
ll
e
r
P
a
ra
m
e
t
e
rs
Op
ti
m
i
z
a
ti
o
n
b
y
u
sin
g
G
e
n
e
ti
c
A
l
g
o
rit
h
m
f
o
r
th
e
C
o
n
tr
o
l
o
f
In
v
e
rted
P
e
n
d
u
l
u
m
”
,
3
rd
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
n
tr
o
l,
E
n
g
i
n
e
e
rin
g
a
n
d
I
n
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
(
CEIT
)
,
T
le
m
c
e
n
,
A
lg
e
ria
,
2
0
1
5
,
p
p
.
1
-
6
.
[1
7
]
Kh
w
a
n
-
On
S
,
Ku
lw
o
ra
w
a
n
ich
p
o
n
g
T
,
S
rik
a
e
w
A
,
S
u
ji
tj
o
r
n
S
,
“
Ne
u
ro
-
tab
u
-
f
u
z
z
y
C
o
n
tro
ll
e
r
t
o
S
tab
il
ize
a
n
In
v
e
rted
P
e
n
d
u
l
u
m
S
y
ste
m
”
,
IEE
E
Reg
io
n
1
0
C
o
n
fer
e
n
c
e
T
ENCON
,
2
0
0
4
,
v
o
l.
5
0
0
,
p
p
.
5
6
2
-
5
6
5
.
[1
8
]
L
e
e
GH
,
Ju
n
g
S
,
“
Co
n
tr
o
l
o
f
In
v
e
rted
P
e
n
d
u
l
u
m
S
y
ste
m
u
sin
g
a
N
e
u
ro
-
f
u
z
z
y
C
o
n
tro
ll
e
r
f
o
r
In
t
e
ll
ig
e
n
t
Co
n
tr
o
l
E
d
u
c
a
ti
o
n
”
,
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
M
e
c
h
a
tro
n
ics
a
n
d
Au
t
o
ma
t
io
n
,
T
a
k
a
m
a
tsu
,
Ja
p
a
n
,
2
0
0
8
,
p
p
.
9
6
5
-
970
.
[1
9
]
Jia
X
,
Da
i
Y,
M
e
m
o
n
ZA
,
“
A
d
a
p
ti
v
e
N
e
u
ro
-
f
u
z
z
y
In
fe
re
n
c
e
S
y
st
e
m
D
e
sig
n
o
f
In
v
e
rted
P
e
n
d
u
l
u
m
S
y
ste
m
o
n
a
n
In
c
li
n
e
d
Ra
il
”
,
S
e
c
o
n
d
W
RI
Gl
o
b
a
l
Co
n
g
re
ss
o
n
I
n
telli
g
e
n
t
S
y
ste
m
s
,
W
u
h
a
n
,
C
h
in
a
,
2
0
1
0
,
v
o
l.
3
,
p
p
.
1
3
7
-
1
4
1
.
[2
0
]
Ba
c
h
ir
O,
Zo
u
b
ir
A
F
,
“
A
d
a
p
ti
v
e
N
e
u
ro
-
f
u
z
z
y
In
fe
re
n
c
e
S
y
st
e
m
b
a
se
d
C
o
n
tro
l
o
f
P
u
m
a
6
0
0
Ro
b
o
t
M
a
n
ip
u
lato
r
”
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
2
0
1
2
F
e
b
1
,
v
o
l.
2
,
n
o
.
1
, p
.
90.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
M
o
h
a
m
m
e
d
Al
-
M
e
k
h
la
fi
w
a
s
b
o
rn
i
n
1
9
9
0
i
n
Ed
in
b
u
rg
h
,
S
c
o
tl
a
n
d
.
He
re
c
e
iv
e
d
h
is
BS
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
(
m
e
c
h
a
tro
n
ics
)
in
2
0
1
5
,
a
n
d
t
h
e
M
.
E
n
g
in
e
lec
tri
c
a
l
e
n
g
in
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rin
g
(
m
e
c
h
a
tro
n
i
c
s
a
n
d
a
u
t
o
m
a
ti
c
c
o
n
tro
l)
i
n
2
0
1
7
f
r
o
m
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
lay
sia
(UT
M
),
M
a
la
y
sia
.
His
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
c
o
n
tro
l
,
a
u
to
n
o
m
o
u
s
m
o
b
il
e
ro
b
o
t,
a
rti
f
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l
in
telli
g
e
n
c
e
a
n
d
r
o
b
o
ti
c
s.
H
e
r
m
a
n
W
a
h
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,
Dr
.
re
c
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d
t
h
e
B.
En
g
.
in
e
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tri
c
a
l
e
n
g
in
e
e
rin
g
(in
stru
m
e
n
tatio
n
a
n
d
c
o
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tr
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in
2
0
0
0
,
a
n
d
M.
E
n
g
in
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tri
c
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l
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(m
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c
h
a
tro
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ics
a
n
d
a
u
t
o
m
a
ti
c
c
o
n
tro
l)
i
n
2
0
0
7
f
ro
m
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
En
g
in
e
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rin
g
,
Un
iv
e
r
siti
T
e
k
n
o
lo
g
i
M
a
la
y
sia
(
UT
M
),
M
a
la
y
sia
.
He
th
e
n
re
c
e
iv
e
d
h
is
P
h
.
D
f
ro
m
Un
iv
e
rsit
y
o
f
T
e
c
h
n
o
lo
g
y
,
S
y
d
n
e
y
(UT
S
),
A
u
stra
li
a
in
2
0
1
3
,
w
h
ich
h
i
s
w
o
rk
f
o
c
u
se
s
o
n
th
e
c
o
m
p
u
tatio
n
a
l
i
n
telli
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t
a
p
p
r
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a
c
h
in
th
e
a
p
p
li
c
a
ti
o
n
o
f
a
ir
q
u
a
li
ty
c
o
n
tro
l
a
n
d
p
re
d
ictio
n
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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&
C
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m
p
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n
g
I
SS
N:
2088
-
8708
A
d
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p
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(
Mo
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A
.
A
.
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-
Mekh
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)
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Ab
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f
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lo
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M
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lay
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(
UT
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),
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la
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sia
.
Evaluation Warning : The document was created with Spire.PDF for Python.