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8792
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ad
iatio
n
v
alu
es
[
4
-
8
]
.
Si
n
ce
ea
ch
tec
h
n
iq
u
e
h
as
d
i
f
f
er
en
t
t
h
eo
r
etica
l
b
asis
,
th
e
o
b
tain
ed
r
esu
lt
s
ar
e
also
u
s
u
a
ll
y
d
i
f
f
er
e
n
t.
So
m
e
tec
h
n
iq
u
es
m
a
y
b
e
m
o
r
e
s
u
itab
le
t
h
an
o
th
er
s
f
o
r
a
s
p
ec
if
ic
s
y
s
te
m
o
r
co
m
p
o
n
e
n
t.
T
h
e
u
s
e
o
f
t
h
e
Ar
tif
icial
Ne
u
r
al
Net
w
o
r
k
s
(
ANN)
tech
n
iq
u
e
h
a
s
s
h
o
w
n
it
s
ef
f
ic
ien
c
y
to
s
o
lv
e
co
m
p
le
x
n
o
n
li
n
ea
r
p
r
o
b
lem
s
,
esp
ec
iall
y
a
s
an
e
s
ti
m
atio
n
to
o
l
f
o
r
p
r
ed
ictin
g
d
esire
d
p
ar
am
eter
s
f
r
o
m
i
n
p
u
t
s
w
it
h
o
u
t
a
k
n
o
w
n
r
elatio
n
[
9
]
.
P
r
e
d
ictio
n
b
ased
o
n
A
NN
te
ch
n
iq
u
e
h
a
s
b
ee
n
u
s
ed
to
s
o
lv
e
s
ev
er
al
p
r
o
b
le
m
s
r
elate
d
to
r
en
e
w
ab
le
e
n
er
g
y
an
d
p
h
o
to
v
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lta
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s
y
s
te
m
s
i.
e.
th
e
e
s
ti
m
atio
n
o
f
th
e
o
p
tim
u
m
tilt
an
g
le
o
f
p
h
o
to
v
o
ltaic
p
an
el
in
o
r
d
er
to
o
b
tain
th
e
m
a
x
i
m
u
m
o
f
s
o
lar
en
er
g
y
[
1
0
]
,
th
e
p
r
ed
ictio
n
o
f
n
ex
t
d
a
y
p
r
o
d
u
ce
d
p
o
w
er
[
1
1
]
.
T
h
e
o
b
j
ec
tiv
e
o
f
th
is
w
o
r
k
is
to
p
r
o
p
o
s
e
an
ap
p
r
o
ac
h
f
o
r
th
e
Ver
tical
So
la
r
I
r
r
ad
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n
(
VSI
)
p
r
ed
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n
b
ased
o
n
an
A
N
N
m
o
d
el
b
y
t
h
e
u
s
e
o
f
m
eteo
r
o
lo
g
ical
m
ea
s
u
r
es.
2.
SO
L
AR
P
O
T
E
N
T
I
A
L
I
N
A
L
G
E
R
I
A
A
l
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s
en
d
o
w
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w
it
h
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h
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g
h
s
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lar
p
o
ten
tial.
As
ca
n
b
e
d
ed
u
ce
d
f
r
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m
T
ab
le
1
,
th
e
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e
ar
l
y
m
ea
n
s
u
n
s
h
i
n
e
d
u
r
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n
v
ar
ies
f
r
o
m
a
lo
w
o
f
2
6
5
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h
o
n
t
h
e
c
o
astal
ar
ea
to
3
5
0
0
h
in
t
h
e
Sah
ar
a
(
So
u
th
)
.
T
h
e
p
o
ten
tial
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f
d
ail
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s
o
lar
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m
p
o
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ta
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t.
I
t
v
ar
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f
r
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m
a
lo
w
av
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ag
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o
f
4
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6
6
k
W
h
/m
2
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n
th
e
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o
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th
to
a
m
ea
n
v
alu
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o
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2
6
k
W
h
/
m
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I
t
m
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t
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ea
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y
p
o
ten
tial
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n
8
6
%
o
f
th
e
ter
r
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r
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i
s
o
f
t
h
e
o
r
d
er
o
f
2
6
5
0
k
W
h
/
m
2
.
T
h
e
an
n
u
a
l
a
v
er
ag
e
d
ail
y
s
u
m
s
o
f
s
o
lar
r
ad
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n
t
h
at
ca
n
b
e
u
tili
z
ed
f
o
r
p
h
o
to
v
o
ltaic
ap
p
licatio
n
s
in
Af
r
ica
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
T
h
ese
v
alu
es
r
an
g
e
f
r
o
m
3
0
0
0
–
7
0
0
0
W
h
/
m
2
/d
a
y
[
3
]
.
T
ab
le
1
.
So
lar
P
o
ten
tial in
A
lg
er
ia
[
1
2
]
A
r
e
a
s
C
o
a
st
a
l
a
r
e
a
H
i
g
h
p
l
a
t
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a
u
S
a
h
a
r
a
T
o
t
a
l
S
u
r
f
a
c
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(
%)
4
10
86
1
0
0
A
r
e
a
(
K
m
2
)
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5
,
2
7
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2
3
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,
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1
A
v
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h
/
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R
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d
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K
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1
7
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P
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t
i
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g
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TW
h
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4
4
3
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9
6
1
2
4
0
.
8
9
1
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,
8
7
0
.
6
3
1
6
,
5
5
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.
4
8
Fig
u
r
e
1
.
An
n
u
al
m
ea
n
g
lo
b
al
r
ad
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n
r
ec
eiv
ed
o
n
a
h
o
r
izo
n
tal
s
u
r
f
ac
e
[
1
3
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
p
p
l P
o
w
er
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n
g
I
SS
N:
2252
-
8792
E
s
tima
tio
n
o
f d
a
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ve
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tica
l so
la
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tio
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lo
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ica
l d
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a
(
Mo
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en
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ia
n
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45
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
3
.
1
.
Art
if
ici
a
l neura
l net
w
o
r
ks
f
o
r
predict
io
n v
er
t
ica
l so
la
r
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dia
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io
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T
h
e
ch
o
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o
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th
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N
tech
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i
q
u
e
is
d
u
e
to
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s
ab
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to
lear
n
f
r
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s
ex
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ce
s
o
f
a
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r
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th
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ex
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allo
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n
k
in
g
t
h
e
o
u
tp
u
t to
th
e
i
n
p
u
t
s
[
1
4
]
.
A
N
Ns
co
n
s
i
s
t
o
f
co
n
n
ec
te
d
elem
e
n
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lled
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r
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lates
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tp
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t
b
ased
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t
h
e
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f
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m
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it
r
ec
ei
v
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ac
h
n
e
u
r
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-
n
eu
r
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b
o
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d
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s
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o
ciate
d
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it
h
a
w
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t.
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h
e
d
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elo
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m
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w
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tag
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,
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f
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ar
ch
itect
u
r
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an
d
th
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id
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n
ti
f
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ca
tio
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o
f
t
h
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p
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m
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s
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h
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p
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ti
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o
f
th
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m
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eter
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b
y
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ch
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k
(
o
r
Mu
lti
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L
a
y
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P
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ce
p
tr
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n
ML
P
)
.
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t
co
n
s
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t
s
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n
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r
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s
d
is
tr
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f
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d
to
th
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n
eu
r
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f
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ac
en
t la
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s
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L
a
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s
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et
w
ee
n
th
e
i
n
p
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t a
n
d
o
u
tp
u
t a
r
e
ca
lled
"
h
id
d
en
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er
s
"
[
1
5
,
1
6
]
.
I
t
is
ca
p
ab
le
to
m
o
d
el
a
n
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;
h
id
d
en
la
y
er
(
s
)
an
d
an
o
u
tp
u
t
la
y
er
[
1
7
]
.
P
r
ed
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o
n
o
f
t
h
e
v
er
tical
s
o
lar
ir
r
ad
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n
ca
n
b
e
ad
d
r
ess
ed
u
s
i
n
g
th
e
M
L
P
.
Fi
g
u
r
e
2
s
h
o
w
s
t
h
is
t
y
p
e
o
f
ar
c
h
itect
u
r
e:
in
p
u
ts
(
Me
teo
r
o
lo
g
ical
d
at
a)
,
a
h
id
d
en
la
y
er
an
d
a
n
o
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t
p
u
t
la
y
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w
it
h
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e
n
eu
r
o
n
.
Fig
u
r
e
2
.
ML
P
w
it
h
o
n
e
h
id
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la
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er
Ma
th
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m
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y
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f
th
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tr
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s
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f
is
id
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tical
f
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all
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r
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e
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f
t
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t o
f
t
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M
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P
w
it
h
o
n
e
h
i
d
d
en
la
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is
g
i
v
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n
b
y
:
0
j
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j
w
x
w
h
An
d
0
1
)
(
W
h
W
f
x
y
j
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j
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(
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1
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ji
w
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w
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th
e
h
id
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d
th
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t la
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.
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h
e
ac
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n
f
u
n
c
tio
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f
ca
n
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e
an
y
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e,
b
u
t
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ac
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d
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p
ar
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lar
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e
n
p
er
f
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m
i
n
g
s
u
p
er
v
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s
ed
lear
n
in
g
,
i
t
i
s
n
ec
ess
ar
y
to
h
av
e
a
co
n
ti
n
u
o
u
s
a
n
d
co
m
p
letel
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d
i
f
f
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t
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le
f
u
n
ct
io
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.
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h
er
e
ar
e
s
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er
al
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t
iv
at
io
n
f
u
n
ct
io
n
s
.
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n
t
h
is
s
t
u
d
y
o
u
r
ch
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ice
is
a
s
i
g
m
o
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f
u
n
ct
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h
id
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en
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er
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.
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p
er
v
is
e
d
lear
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n
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eter
m
in
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th
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et
w
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r
k
w
eig
h
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h
ic
h
m
i
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i
m
ize
o
n
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l
th
e
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ata
o
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t
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e
lear
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d
ataset
,
th
e
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iatio
n
s
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et
w
ee
n
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h
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o
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tp
u
t
v
al
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es
(
m
ea
s
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r
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al
u
es)
an
d
th
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ted
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[4
]
Ha
i
y
in
g
Do
n
g
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Ya
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g
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ru
i
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i,
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A
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ian
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tatio
n
"
,
T
E
L
KOM
NIKA
In
d
o
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1
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6
,
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a
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2
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4
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[5
]
M
u
sta
p
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a
E
ly
a
q
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a
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in
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lal,
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a
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c
teristics
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Ho
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,
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ter
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)
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p
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2
5
5
7
-
2
5
7
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c
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2
0
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6
.
[6
]
K.
Da
h
m
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n
i,
R.
Diz
e
n
e
,
G
.
No
tt
o
n
,
C.
P
a
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.
Vo
y
a
n
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.
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.
Niv
e
t,
"
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m
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5
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m
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irrad
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N
(A
rti
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Ne
u
ra
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Ne
t
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rk
)
m
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l,
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p
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4
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3
8
1
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n
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2
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4
.
[7
]
J.L
.
De
S
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,
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.
B.
Ly
r
a
,
C.
M
.
Do
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.
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.
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.
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ib
a
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ra
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.
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L
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s,
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p
iri
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lag
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tate
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[8
]
A
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il
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p
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terim
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]
R.
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[1
1
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M
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fi
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ter
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2
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T
.
Bo
u
k
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li
a
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M
.
S
.
M
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ib
a
h
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rm
a
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g
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Ren
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1
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p
.
2
8
8
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9
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a
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3
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CDER
,
Ce
n
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e
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v
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p
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n
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En
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lab
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ww
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z
),
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tt
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4
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S.
Reh
m
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.
M
o
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e
s,
"
A
rti
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h
u
m
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it
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y
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l.
3
6
,
p
p
.
5
7
1
–
5
7
6
,
2
0
0
8
.
[1
5
]
M
.
Be
n
z
ian
e
a
n
d
M
.
Bo
u
a
m
a
r
,
"
A
so
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tatio
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ter
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.
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to
ma
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s E
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p
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6
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C.
Re
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.
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ti
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A
.
G
a
tt
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,
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rti
f
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tratin
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m
,
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p
.
9
9
9
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2
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7
]
N.
Ku
m
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r,
S
.
P
.
S
h
a
rm
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,
U.
K.
S
i
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Na
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k
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telli
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,
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ter
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1
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8
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O.
A
ss
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s,
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Bo
u
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S
.
F
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ta
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lm
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.
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u
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Dje
lf
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lg
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,
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2
0
1
4
I
n
ter
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ti
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Co
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Co
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site
M
a
ter
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&
Ren
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ICCM
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p
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Un
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’sila,
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sp
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De
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Un
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s.
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