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1
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[
2
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b
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[
4
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a
m
p
li
n
g
p
o
in
ts
,
w
h
ic
h
ar
e
t
y
p
icall
y
d
eter
m
i
n
ed
u
s
i
n
g
ex
p
e
r
i
m
en
tal
d
esi
g
n
m
e
th
o
d
s
[
5
]
.
A
SM
e
x
p
o
s
es
t
h
e
s
y
s
te
m
’
s
in
p
u
t
-
o
u
tp
u
t
r
elatio
n
s
h
ip
t
h
r
o
u
g
h
a
s
i
m
p
le
m
ath
e
m
atica
l
f
u
n
ctio
n
[
3
]
.
T
h
u
s
t
h
e
s
i
m
u
lati
o
n
ti
m
e
f
o
r
SM
is
les
s
th
a
n
t
h
at
o
f
t
h
e
ac
t
u
al
s
i
m
u
latio
n
m
o
d
el.
R
ec
en
t
l
y
,
a
s
s
t
u
d
ied
in
[
6
]
,
SM
h
ad
b
ee
n
u
s
ed
to
o
p
ti
m
i
ze
v
ar
io
u
s
t
y
p
e
o
f
s
y
s
te
m
,
i
n
clu
d
ed
th
e
n
o
n
li
n
ea
r
s
y
s
te
m
.
So
m
e
o
f
t
h
e
s
y
s
te
m
s
t
h
at
w
er
e
s
u
cc
e
s
s
f
u
ll
y
o
p
ti
m
ized
u
s
i
n
g
t
h
e
S
M
tech
n
iq
u
e
ar
e
t
h
e
C
ar
tesi
a
n
C
o
o
r
d
in
ate
s
C
o
n
tr
o
l
o
f
Ho
v
er
cr
af
t
S
y
s
te
m
[
7
]
an
d
th
e
u
n
m
an
n
ed
u
n
d
er
w
a
ter
v
eh
icle
[
8
]
,
[
9
]
.
T
h
r
o
u
g
h
t
h
eir
s
tu
d
y
,
th
e
y
al
s
o
h
ad
p
r
o
v
ed
th
at
t
h
e
SM
te
ch
n
iq
u
e
ca
n
o
p
ti
m
ize
v
ar
io
u
s
t
y
p
es
o
f
co
n
tr
o
ller
p
ar
am
eter
s
,
f
o
r
ex
a
m
p
le,
th
e
f
u
zz
y
lo
g
ic
co
n
tr
o
ller
an
d
th
e
P
I
D
co
n
tr
o
ller
.
T
h
e
co
r
e
o
f
SM
is
a
m
eta
m
o
d
el
th
at
g
i
v
es
th
e
p
r
ed
ictio
n
o
f
a
s
y
s
te
m
’
s
o
u
tp
u
t.
A
l
th
o
u
g
h
t
h
e
o
u
tp
u
t
f
r
o
m
m
eta
m
o
d
el
is
a
n
ap
p
r
o
x
im
ate
o
f
ac
tu
al
m
ea
s
u
r
e
m
e
n
t
o
f
co
m
p
le
x
m
o
d
el,
it
g
i
v
es
a
g
o
o
d
ap
p
r
o
x
im
a
te
o
f
th
e
ac
t
u
al
v
al
u
e.
T
h
e
e
v
alu
a
ti
o
n
o
f
o
u
tp
u
t
v
a
lu
e
is
f
a
s
t
a
n
d
p
r
o
v
id
es
en
o
u
g
h
i
n
f
o
r
m
atio
n
d
u
r
in
g
d
esi
g
n
p
h
a
s
e
o
f
a
s
y
s
te
m
[
1
0
]
.
E
x
a
m
p
les
o
f
m
e
ta
m
o
d
el
ar
e
R
ad
ial
B
asis
Fu
n
c
tio
n
s
Neu
r
al
Net
w
o
r
k
s
(
R
B
FNN)
,
Kr
ig
i
n
g
Mo
d
els
(
KR
)
,
P
o
l
y
n
o
m
ial
R
e
g
r
ess
io
n
(
P
R
)
,
M
u
lti
v
ar
iate
Ad
ap
tiv
e
R
e
g
r
e
s
s
io
n
Sp
lin
e
s
(
MA
R
S),
a
n
d
S
u
p
p
o
r
t
Vec
to
r
Ma
ch
in
e
s
(
SVM)
.
I
n
c
o
m
p
ar
is
o
n
,
R
B
FNN
s
h
o
w
s
a
g
en
er
all
y
b
etter
p
er
f
o
r
m
a
n
ce
.
B
ased
o
n
d
if
f
er
en
t
t
y
p
es o
f
p
r
o
b
lem
s
(
i.e
.
,
d
if
f
er
e
n
t o
r
d
er
s
o
f
n
o
n
li
n
ea
r
it
y
an
d
p
r
o
b
lem
s
ca
les)
it
is
co
n
clu
d
ed
th
at
R
B
FNN
i
s
t
h
e
m
o
s
t
d
ep
e
n
d
ab
le
m
et
h
o
d
in
m
o
s
t
s
it
u
atio
n
s
i
n
ter
m
s
o
f
ac
cu
r
ac
y
a
n
d
r
o
b
u
s
t
n
e
s
s
[
1
1
]
.
I
n
t
h
is
p
r
o
j
ec
t,
a
R
B
FNN
w
a
s
u
s
ed
as
th
e
m
e
t
a
m
o
d
el
to
ap
p
r
o
x
i
m
a
te
t
h
e
m
ap
p
in
g
o
f
t
h
e
co
n
tr
o
ller
g
a
in
s
an
d
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
2.
M
O
DE
L
I
N
G
O
F
T
H
E
SYS
T
E
M
S
2
.
1
.
Ra
dia
l B
a
s
is
F
un
ct
io
n Ne
ur
a
l N
et
wo
rk
R
ad
ial
B
asis
F
u
n
ct
io
n
Neu
r
al
Net
w
o
r
k
(
R
B
F
NN)
w
a
s
u
s
e
d
as
th
e
Me
ta
m
o
d
el
to
ap
p
r
o
x
i
m
ate
th
e
m
ap
p
in
g
o
f
t
h
e
co
n
tr
o
ller
p
ar
a
m
eter
s
a
n
d
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
T
h
e
r
ad
ial
b
asis
f
u
n
ctio
n
s
w
er
e
f
ir
s
t
u
s
ed
to
d
esig
n
A
r
t
if
ic
ial
Neu
r
al
Net
wo
r
k
s
in
1
9
8
8
b
y
B
r
o
o
m
h
ea
d
a
n
d
L
o
w
e
[
1
2
]
.
T
h
e
ar
ch
itectu
r
e
o
f
th
e
R
B
F
NN
u
s
ed
in
t
h
i
s
w
o
r
k
is
il
lu
s
tr
ated
in
Fi
g
u
r
e
1
.
Fig
u
r
e
1
.
R
ad
ia
l B
asis
F
u
n
ct
io
n
Neu
r
al
Net
w
o
r
k
T
h
e
n
et
w
o
r
k
co
n
s
i
s
ts
o
f
t
h
r
e
e
la
y
er
s
:
an
in
p
u
t
la
y
er
,
a
h
i
d
d
en
la
y
er
a
n
d
an
o
u
tp
u
t
la
y
er
.
Her
e,
R
d
en
o
tes
th
e
n
u
m
b
er
o
f
in
p
u
ts
w
h
ile
Q
t
h
e
n
u
m
b
er
o
f
o
u
tp
u
t
s
.
E
q
u
atio
n
(
1
)
is
u
s
ed
to
ca
lcu
late
t
h
e
o
u
tp
u
t
o
f
th
e
R
B
F NN
f
o
r
Q
=
1
,
th
e
o
u
t
p
u
t o
f
th
e
R
B
FNN
i
n
Fi
g
u
r
e
1
is
ca
lcu
lated
ac
co
r
d
in
g
to
1
1
2
1
,
S
kk
k
x
w
w
x
c
(
1
)
W
h
er
e
1
R
x
R
is
an
i
n
p
u
t
v
ec
to
r
,
.
is
a
b
asis
f
u
n
ctio
n
,
2
.
d
en
o
tes
th
e
E
u
clid
ea
n
n
o
r
m
,
1
k
w
ar
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th
e
w
ei
g
h
ts
in
th
e
o
u
tp
u
t
la
y
er
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S1
i
s
t
h
e
n
u
m
b
er
o
f
n
e
u
r
o
n
s
(
a
n
d
ce
n
ter
s
)
in
th
e
h
id
d
en
la
y
er
a
n
d
1
R
k
c
R
ar
e
th
e
R
B
F c
en
ter
s
in
t
h
e
i
n
p
u
t v
ec
to
r
s
p
ac
e.
E
q
u
atio
n
(
1
)
ca
n
also
b
e
w
r
itte
n
as E
q
u
a
tio
n
(
2
)
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
.
1
,
Feb
r
u
ar
y
2
0
1
8
:
5
5
6
–
5
6
5
558
,
(
)
T
x
w
x
w
(
2
)
W
h
er
e
b
asis
f
u
n
ctio
n
i
n
E
q
u
at
io
n
(
3
)
1
1
1
1
T
SS
x
x
c
x
c
(
3
)
An
d
w
eig
h
t la
y
er
in
E
q
u
at
io
n
(
4
)
1
1
1
2
1
1
T
S
w
w
w
w
(
4
)
T
h
e
o
u
tp
u
t o
f
th
e
n
eu
r
o
n
in
a
h
id
d
en
la
y
er
i
s
a
n
o
n
l
in
ea
r
f
u
n
ctio
n
o
f
t
h
e
d
is
tan
ce
g
i
v
en
b
y
E
q
u
atio
n
(
5
)
:
22
/
x
xe
(
5
)
W
h
er
e
β
is
t
h
e
s
p
r
ea
d
p
ar
am
et
er
o
f
th
e
R
B
F.
Fo
r
tr
ain
in
g
,
th
e
least
s
q
u
ar
es
f
o
r
m
u
la
w
a
s
u
s
ed
to
f
in
d
th
e
s
ec
o
n
d
la
y
er
w
ei
g
h
ts
w
h
i
le
th
e
ce
n
ter
s
ar
e
s
et
u
s
in
g
t
h
e
av
ailab
le
d
ata
s
a
m
p
les.
T
h
u
s
,
th
e
ap
p
r
o
ac
h
o
f
P
ar
eto
-
b
ased
Su
r
r
o
g
ate
Mo
d
el
in
g
A
l
g
o
r
ith
m
(
P
SM
A
)
f
o
r
m
u
ltio
b
j
ec
tiv
e
o
p
ti
m
izatio
n
as
s
u
m
m
ar
ized
i
n
[
6
-
9
]
w
a
s
u
s
ed
in
t
h
is
p
r
o
j
ec
t.
2
.
2
.
F
o
rc
ed
Circ
ula
t
io
n E
v
a
po
ra
t
o
r
I
n
ad
d
itio
n
,
a
m
eta
m
o
d
elin
g
ap
p
r
o
ac
h
f
o
r
PID
co
n
tr
o
lle
r
in
an
ev
ap
o
r
ato
r
p
r
o
ce
s
s
h
as
b
ee
n
s
u
cc
e
s
s
f
u
ll
y
p
r
ese
n
ted
i
n
[
1
3
]
,
[
1
4
]
.
Fig
u
r
e
2
s
h
o
w
s
t
h
e
f
o
r
ce
d
cir
cu
latio
n
e
v
ap
o
r
ato
r
d
er
iv
ed
b
y
Ne
w
e
ll
a
n
d
L
ee
[
1
5
]
in
1
9
8
9
.
T
h
is
ev
ap
o
r
ato
r
h
as
b
ec
o
m
e
a
w
ell
-
k
n
o
w
n
a
n
d
v
er
y
d
if
f
ic
u
lt
b
en
c
h
m
ar
k
u
s
ed
b
y
co
n
tr
o
l
en
g
i
n
ee
r
s
to
ev
al
u
ate
t
h
eir
m
et
h
o
d
o
lo
g
ies.
A
f
ee
d
s
tr
ea
m
e
n
ter
s
t
h
e
ev
ap
o
r
ato
r
w
it
h
co
n
ce
n
tr
at
io
n
X1
,
t
e
m
p
er
atu
r
e
T
1
an
d
f
lo
w
r
at
e
F1
.
I
t
w
ill
m
i
x
w
it
h
r
ec
ir
cu
latio
n
liq
u
o
r
,
w
h
ic
h
is
p
u
m
p
ed
th
r
o
u
g
h
t
h
e
ev
ap
o
r
ato
r
at
f
lo
w
r
ate
F3
.
T
h
e
e
v
ap
o
r
ato
r
its
elf
is
a
h
ea
t
ex
ch
a
n
g
er
,
w
h
ich
is
h
ea
ted
b
y
s
tea
m
f
l
o
w
in
g
a
t
a
r
ate
F1
0
0
,
w
it
h
te
m
p
er
atu
r
e
T
1
0
0
an
d
p
r
ess
u
r
e
P
1
0
0
.
T
h
e
m
i
x
t
u
r
e
o
f
f
ee
d
a
n
d
r
ec
ir
cu
latio
n
liq
u
o
r
b
o
ils
in
s
id
e
t
h
e
h
ea
t
ex
c
h
a
n
g
er
,
an
d
th
e
r
esu
lti
n
g
m
i
x
t
u
r
e
o
f
v
ap
o
r
an
d
liq
u
id
en
ter
s
th
e
s
ep
ar
at
o
r
,
w
h
ic
h
th
e
liq
u
id
lev
el
is
L
2
.
T
h
e
o
p
er
atin
g
p
r
e
s
s
u
r
e
in
s
id
e
th
e
ev
ap
o
r
ato
r
is
P
2
.
So
m
e
p
o
r
tio
n
o
f
liq
u
id
f
r
o
m
s
ep
ar
ato
r
d
r
aw
n
o
u
t
as
p
r
o
d
u
ct
w
it
h
co
n
ce
n
t
r
atio
n
X2
,
w
i
th
f
lo
w
r
ate
F2
an
d
te
m
p
er
at
u
r
e
T
2
;
m
o
s
t
o
f
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T
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T
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to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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N:
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T
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e
NSG
A
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I
[
1
6
]
is
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elec
t
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m
p
ar
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P
SM
A
b
ec
au
s
e
o
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ated
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y
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g
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ith
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co
n
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m
ar
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in
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ab
le
6
.
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h
e
ch
o
ice
o
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v
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r
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tatio
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w
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s
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s
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r
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h
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.
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d
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etic
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ater
ial
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b
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s
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a
m
n
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th
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n
u
m
b
er
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m
eter
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in
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Si
m
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eter
(
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an
d
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t
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p
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eter
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d
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.
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ab
le
6
.
NSGA
-
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r
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ti
m
e
p
ar
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m
eter
s
R
e
p
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a
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6
M
u
t
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t
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a
b
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l
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t
y
0
.
1
6
7
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B
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p
a
r
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me
t
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r
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M
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4.
RE
SU
L
T
S
A
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AL
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S
4
.
1
.
Si
m
ula
t
io
n Re
s
ult
o
f
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A
Fig
u
r
e
4
s
h
o
w
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h
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s
i
m
u
latio
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r
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lt
o
f
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2
an
d
L
2
u
s
in
g
in
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ets
w
ith
4
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to
tal
n
u
m
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er
o
f
d
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n
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.
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u
r
e
4
.
I
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o
f
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ato
r
lev
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d
o
p
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atin
g
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r
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u
r
e
f
o
r
in
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ata
s
ets.
0
50
100
150
200
250
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400
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I
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I
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I
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T
h
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ter
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ter
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ai
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eter
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w
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5
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ata
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ets.
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h
e
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lt
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o
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u
r
e
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.
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h
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ated
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d
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en
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I
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atin
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p
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f
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wh
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A
tr
ai
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g
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d
te
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p
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ce
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n
d
p
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ed
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o
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t
h
r
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g
h
s
u
r
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o
g
ate
m
o
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e
lin
g
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T
h
e
P
SMA
ap
p
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o
ac
h
u
s
clea
r
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a
u
s
e
f
u
l
ap
p
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ac
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d
th
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ill b
ec
o
m
e
m
o
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e
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ig
n
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ica
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t f
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lar
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er
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o
f
f
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e
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licated
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a
s
a
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ca
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e,
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A
u
s
ed
to
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p
ti
m
ize
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p
ar
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m
eter
g
ain
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f
P
I
D
co
n
tr
o
ller
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r
r
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m
a
te
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et
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ar
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eter
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in
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it
h
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r
t
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i
m
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lat
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h
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te
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ca
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e
d
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if
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etter
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eq
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th
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ex
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m
p
le,
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ata
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et
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w
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cr
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t
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ased
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m
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in
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ap
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to
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h
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p
les
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eq
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p
le
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in
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p
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x
p
er
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n
tal
D
esig
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tech
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iq
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s
s
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c
h
as
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t
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ase
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d
C
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tec
h
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iq
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e.
I
t
is
e
n
v
is
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g
ed
t
h
at
a
m
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r
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s
tr
ateg
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d
ata
lo
ca
tio
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il
l
allo
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th
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cr
ea
tio
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f
a
m
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ac
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u
r
ate
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u
r
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ate
m
o
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e
lin
g
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s
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n
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les
s
d
ata,
t
h
er
ef
o
r
e,
less
ti
m
e
r
eq
u
ir
ed
to
esti
m
ate
th
e
b
est
co
n
tr
o
ller
p
ar
am
eter
s
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
is
p
r
o
j
ec
t
is
s
u
p
p
o
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tin
g
b
y
Min
i
s
tr
y
o
f
Scie
n
ce
,
T
ec
h
n
o
l
o
g
y
an
d
I
n
n
o
v
atio
n
(
MO
ST
I
)
e
-
Scie
n
ce
Fu
n
d
R
e
s
ea
r
ch
Gr
a
n
t.
Sp
ec
ia
l
th
a
n
k
s
to
Facu
l
t
y
o
f
E
lectr
i
ca
l
E
n
g
in
ee
r
i
n
g
,
U
n
iv
er
s
iti
T
ek
n
o
lo
g
i
Ma
la
y
s
i
a
(
UT
M)
,
also
E
lectr
ical
Dep
ar
t
m
en
t U
n
iv
er
s
it
y
M
u
h
a
m
m
ad
i
y
ah
o
f
Ma
lan
g
(
UM
M)
f
o
r
g
iv
in
g
f
u
ll s
u
p
p
o
r
t a
n
d
co
o
p
er
atio
n
.
A
ls
o
w
ar
m
e
s
t
th
an
k
s
to
r
esear
ch
an
d
d
ev
elo
p
m
en
t
ce
n
tr
e
o
f
UT
M.
T
h
eir
s
u
p
p
o
r
ts
ar
e
g
r
atef
u
ll
y
ac
k
n
o
w
led
g
ed
.
RE
F
E
R
E
NC
E
S
[1
]
Ob
a
y
a
sh
i
S
,
Je
o
n
g
S
,
Ch
ib
a
K.
“
M
u
lt
i
-
Ob
jec
ti
v
e
De
sig
n
Exp
lo
r
a
ti
o
n
t
o
r
Aer
o
d
y
n
a
mic
Co
n
fi
g
u
r
a
ti
o
n
s”
.
In
3
5
t
h
A
I
AA
f
lu
id
d
y
n
a
m
ics
c
o
n
f
e
r
e
n
c
e
a
n
d
e
x
h
ib
i
t
2
0
0
5
Ju
n
(
p
.
4
6
6
6
).
[2
]
Zi
tzle
r
E,
De
b
K,
T
h
iele
L
.
“
Co
m
p
a
riso
n
o
f
M
u
lt
io
b
jec
ti
v
e
Ev
o
lu
ti
o
n
a
ry
A
lg
o
rit
h
m
s:
E
m
p
iri
c
a
l
Re
su
lt
s
”
.
Evo
lu
ti
o
n
a
ry
Co
mp
u
t
a
ti
o
n
.
2
0
0
0
;
8
(2
):
1
7
3
-
9
5
.
[3
]
M
a
L
,
X
in
K,
L
iu
S
.
“
Us
in
g
R
a
d
ial
Ba
sis
F
u
n
c
ti
o
n
Ne
u
ra
l
Ne
t
wo
rk
s
t
o
Ca
li
b
ra
te
W
a
t
e
r
Qu
a
li
t
y
M
o
d
e
l
”
.
W
o
rl
d
A
c
a
d
e
m
y
o
f
S
c
ien
c
e
,
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
n
v
iro
n
me
n
ta
l,
C
h
e
mi
c
a
l,
Eco
l
o
g
ica
l,
Ge
o
lo
g
ic
a
l
a
n
d
Ge
o
p
h
y
sic
a
l
E
n
g
i
n
e
e
rin
g
.
2
0
0
8
F
e
b
2
5
;2
(
2
):9
-
17.
[4
]
S
a
n
to
s
IR,
S
a
n
to
s
P
R.
“
S
im
u
l
a
ti
o
n
M
e
ta
mo
d
e
ls
f
o
r
M
o
d
e
li
n
g
Ou
tp
u
t
Distrib
u
ti
o
n
P
a
ra
me
t
e
rs
”
.
In
W
in
ter
S
im
u
latio
n
C
o
n
f
e
re
n
c
e
,
2
0
0
7
De
c
9
(
p
p
.
9
1
0
-
9
1
8
).
IEE
E.
[5
]
Kle
ij
n
e
n
J
P
,
S
a
rg
e
n
t
RG
.
“
A
M
e
t
h
o
d
o
l
o
g
y
f
o
r
F
it
ti
n
g
a
n
d
V
a
li
d
a
ti
n
g
M
e
ta
m
o
d
e
ls
i
n
S
im
u
latio
n
”
.
E
u
ro
p
e
a
n
J
o
u
rn
a
l
o
f
Op
e
ra
t
io
n
a
l
Res
e
a
rc
h
.
2
0
0
0
Ja
n
1
;
1
2
0
(1
)
:1
4
-
2
9
.
[6
]
M.
S.
Mo
h
a
m
ed
Ali
,
SS
A
b
d
u
ll
a
h
,
O
sm
a
n
Da
v
id
C.
“
Co
n
tro
ll
e
rs
Op
ti
m
iza
ti
o
n
F
o
r
A
F
lu
id
M
ix
in
g
S
y
ste
m
Us
in
g
M
e
tam
o
d
e
ll
in
g
A
p
p
ro
a
c
h
”
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
S
imu
l
a
ti
o
n
M
o
d
e
ll
i
n
g
.
2
0
0
9
M
a
r
1
;8
(
1
):
4
8
-
59.
[7
]
M
.
S
.
M
o
h
a
m
e
d
A
li
,
S
.
S
.
A
b
d
u
l
lah
,
M
.
A
.
A
h
m
a
d
a
n
d
N.
Ha
m
b
a
li
,
“
Op
ti
miza
ti
o
n
o
f
PID
Co
n
tro
ll
e
rs
fo
r
Ca
rte
sia
n
Co
o
rd
i
n
a
tes
Co
n
tr
o
l
o
f
Ho
v
e
rc
ra
ft
S
y
ste
m
Us
in
g
M
e
ta
mo
d
e
li
n
g
Ap
p
ro
a
c
h
”
,
P
ro
c
e
e
d
i
n
g
s
o
f
th
e
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
P
o
w
e
r
Co
n
tro
l
a
n
d
Op
ti
m
iza
ti
o
n
,
C
h
ian
g
M
a
i,
T
h
a
il
a
n
d
,
J
u
ly
1
8
-
2
0
,
2
0
0
8
.
[8
]
M
.
F
.
N.
S
h
a
h
,
S
.
S
.
A
b
d
u
ll
a
h
,
a
n
d
F
a
ru
q
,
A
.
,
“
M
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
miza
ti
o
n
o
f
re
mo
tely
o
p
e
ra
te
d
v
e
h
icle
c
o
n
tro
l
sy
ste
m
u
sin
g
s
u
rr
o
g
a
te
m
o
d
e
li
n
g
”
in
IEE
E
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
n
t
ro
l
S
y
ste
m
,
Co
m
p
u
ti
n
g
a
n
d
Eg
in
e
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ri
n
g
(ICCS
CE)
,
2
0
1
1
,
p
p
.
1
3
8
-
1
4
3
.
[9
]
F
a
ru
q
A
,
A
b
d
u
ll
a
h
S
S
,
F
a
u
z
i
M
,
No
r
S
.
“
O
p
ti
miza
t
io
n
Of
De
p
t
h
C
o
n
tro
l
Fo
r
U
n
ma
n
n
e
d
U
n
d
e
rwa
te
r
Veh
icle
Us
in
g
S
u
rr
o
g
a
te
M
o
d
e
li
n
g
T
e
c
h
n
i
q
u
e
”
.
In
M
o
d
e
li
n
g
,
S
im
u
latio
n
a
n
d
A
p
p
li
e
d
Op
ti
m
iza
ti
o
n
(ICM
S
A
O),
2
0
1
1
4
t
h
Evaluation Warning : The document was created with Spire.PDF for Python.
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n
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timiz
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A
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565
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tern
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2
0
1
1
A
p
r
1
9
(
p
p
.
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-
7
).
IE
EE
.
[1
0
]
S
.
S
.
A
b
d
u
ll
a
h
&
J
.
C.
A
ll
w
ri
g
h
t,
“
A
n
A
c
ti
v
e
L
e
a
rn
in
g
A
p
p
ro
a
c
h
F
o
r
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d
ial
Ba
sis
F
u
n
c
ti
o
n
Ne
u
ra
l
Ne
tw
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rk
s”
,
J
u
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4
5
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c
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2
0
0
6
:
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7
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6
,
Un
iv
e
rsiti
T
e
k
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o
lo
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i
M
a
la
y
sia
.
[1
1
]
Jin
R,
C
h
e
n
W
,
S
im
p
so
n
T
W
.
“
Co
m
p
a
ra
ti
v
e
S
tu
d
ies
Of
M
e
tam
o
d
e
ll
in
g
T
e
c
h
n
iq
u
e
s
Un
d
e
r
M
u
lt
ip
le
M
o
d
e
ll
i
n
g
Crit
e
ria
”
.
S
tru
c
tu
r
a
l
A
n
d
M
u
lt
i
d
is
c
ip
li
n
a
ry
Op
ti
miza
ti
o
n
.
2
0
0
1
De
c
1
;2
3
(1
)
:1
-
3.
[1
2
]
B
r
o
o
m
h
ea
d
,
D.
S.
an
d
L
o
w
e
D.
“
M
u
lt
i
-
V
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riab
le
F
u
n
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ti
o
n
a
l
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n
terp
o
latio
n
A
n
d
A
d
a
p
ti
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e
N
e
t
wo
rk
s
”
.
Co
mp
lex
S
y
ste
ms
.
;2
:3
2
1
-
55.
[1
3
]
M
.
F
.
N.
S
h
a
h
,
Zain
a
l
M
A
,
F
a
ru
q
A
,
S
S
A
b
d
u
ll
a
h
.
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M
e
tam
o
d
e
li
n
g
A
p
p
ro
a
c
h
F
o
r
P
ID
Co
n
tr
o
ll
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r
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ti
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iza
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o
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In
A
n
Ev
a
p
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ra
to
r
P
ro
c
e
ss
”
.
IEE
E
In
M
o
d
e
li
n
g
,
S
imu
l
a
ti
o
n
a
n
d
Ap
p
li
e
d
Op
ti
miz
a
ti
o
n
(
ICM
S
AO),
2
0
1
1
4
t
h
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
2
0
1
1
A
p
r
1
9
(
p
p
.
1
-
4
).
[1
4
]
M
.
F
.
N.
S
h
a
h
,
S
.
S
.
A
b
d
u
ll
a
h
,
a
n
d
F
a
ru
q
,
A
.
,
“
M
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
o
f
a
n
e
v
a
p
o
ra
to
r
c
o
n
t
ro
l
sy
ste
m
u
sin
g
su
rro
g
a
te
m
o
d
e
li
n
g
”
in
IEE
E
I
n
t
e
rn
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
C
o
n
tr
o
l
S
y
ste
m,
Co
mp
u
ti
n
g
a
n
d
Eg
in
e
e
rin
g
(
ICCS
CE)
,
2
0
1
1
,
p
p
.
1
9
8
–
2
0
3
.
[1
5
]
Ne
w
e
ll
,
R.
B.
a
n
d
L
e
e
,
P
.
L
.
,
“
A
p
p
li
e
d
P
ro
c
e
ss
Co
n
tro
l
:
A
Ca
s
e
S
tu
d
y
”
,
P
r
o
c
e
ss
Co
n
tr
o
l
G
ro
u
p
De
p
a
rtm
e
n
t
o
f
Ch
e
m
ic
a
l
En
g
in
e
e
rin
g
Un
iv
e
rsity
o
f
Qu
e
e
n
sla
n
d
,
A
u
stra
li
a
,
P
re
n
ti
c
e
Ha
ll
,
1
9
8
9
.
[1
6
]
De
b
K,
P
ra
tap
A
,
A
g
a
r
w
a
l
S
,
M
e
y
a
riv
a
n
TA
.
“
A
F
a
st
A
n
d
El
it
ist
M
u
lt
i
o
b
je
c
ti
v
e
Ge
n
e
ti
c
A
lg
o
rit
h
m
:
NS
GA
-
II”
.
IEE
E
T
ra
n
sa
c
ti
o
n
s O
n
Evo
l
u
ti
o
n
a
ry
Co
mp
u
ta
t
io
n
.
2
0
0
2
A
p
r;6
(
2
):
1
8
2
-
9
7
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
A
m
r
u
l
Fa
r
u
q
d
id
Ba
c
h
e
lo
r
En
g
in
e
e
r
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
t
Un
iv
e
rsit
y
o
f
M
u
h
a
m
m
a
d
i
y
a
h
M
a
lan
g
,
In
d
o
n
e
sia
o
n
2
0
0
9
.
He
h
a
s
o
b
tain
e
d
h
is
M
a
ste
r
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
r
siti
Te
k
n
o
lo
g
i
M
a
lay
sia
,
U
T
M
M
a
la
y
s
ia
o
n
2
0
1
3
.
Cu
r
re
n
tl
y
h
e
is
w
o
rk
in
g
a
s
ju
n
io
r
lec
tu
re
r
i
n
EE
De
p
a
rtm
e
n
t
o
f
UMM
.
His
in
tere
ste
d
re
se
a
rc
h
is
a
b
o
u
t
Op
ti
m
iza
ti
o
n
m
e
th
o
d
,
a
rti
f
icia
l
in
telli
g
e
n
t,
e
lec
tro
n
ics
a
n
d
c
o
m
p
u
te
r
n
e
tw
o
rk
.
M
o
h
d
Fa
u
z
i
No
r
S
h
a
h
g
ra
d
u
a
ted
in
Ba
c
h
e
lo
r
o
f
El
e
c
tro
n
ics
En
g
.
(In
d
u
strial
E
lec
tro
n
ics
)
in
Un
iv
e
rsiti
T
e
k
n
ik
a
l
M
a
la
y
sia
M
e
l
a
k
a
(
UT
EM
)
o
n
2
0
0
9
.
L
a
ter
h
e
fu
rth
e
r
h
is
stu
d
y
a
n
d
o
b
tain
e
d
M
a
ste
r
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
la
y
sia
(U
T
M
)
o
n
2
0
1
2
.
Cu
rre
n
tl
y
h
e
is
w
o
rk
in
g
a
s
a
n
s
p
e
c
ialist
e
n
g
in
e
e
r
in
b
a
c
k
-
e
n
d
s
e
m
ico
n
d
u
c
t
o
r
i
n
d
u
stry
w
it
h
f
o
c
u
s o
n
c
o
m
p
u
ter v
isio
n
a
n
d
i
n
sp
e
c
ti
o
n
.
Dr
.
S
h
a
h
r
u
m
S
h
a
h
Abd
u
ll
a
h
d
id
B.
E
n
g
.
(El
e
c
tri
c
a
l)
in
M
c
G
il
l
Un
iv
e
rsit
y
,
M
.
S
c
.
(Co
n
tr
o
l
S
y
st
e
m
s)
in
Un
iv
e
rsit
y
o
f
S
h
e
ffield
,
a
n
d
o
b
tain
e
d
h
is
P
h
.
D
(Co
n
tr
o
l
sy
ste
m
s)
in
Im
p
e
rial
Co
ll
e
g
e
o
f
S
c
ien
c
e
,
T
e
c
h
n
o
lo
g
y
a
n
d
M
e
d
icin
e
,
CEn
g
,
M
I
ET
.
Cu
rre
n
tl
y
h
e
is
w
o
rk
in
g
a
s
S
e
n
io
r
L
e
c
tu
re
r
,
He
a
d
o
f
El
e
c
tro
n
ic
S
y
st
e
m
s
En
g
in
e
e
rin
g
De
p
a
rt
m
e
n
t
a
t
M
a
la
y
sia
-
Ja
p
a
n
In
tern
a
ti
o
n
a
l
In
stit
u
te
o
f
T
e
c
h
n
o
lo
g
y
(M
JIIT
).
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