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C
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1
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p
ed
h
y
b
r
id
m
o
d
els
b
y
co
m
b
in
in
g
t
w
o
o
r
m
o
r
e
A
I
m
et
h
o
d
s
.
T
h
is
i
s
d
o
n
e
to
o
b
tain
a
m
o
r
e
ac
cu
r
at
e
p
r
ed
ictio
n
.
R
esear
ch
er
s
li
k
e
W
u
et
al
[
1
8
]
d
ev
elo
p
ed
a
h
y
b
r
id
m
o
d
el
f
o
r
s
o
lar
r
ad
iatio
n
p
r
ed
ictio
n
b
y
co
m
b
in
i
n
g
Au
to
r
eg
r
ess
i
v
e
Mo
v
in
g
Av
er
ag
e
Mo
d
el
(
AR
M
A
)
a
n
d
T
im
e
Di
v
is
io
n
Neu
r
al
Net
w
o
r
k
(
T
DNN
)
w
h
ich
h
e
f
o
u
n
d
o
u
t
t
h
at
t
h
e
co
m
b
in
at
io
n
o
f
th
e
t
w
o
m
eth
o
d
s
o
u
tp
er
f
o
r
m
s
t
h
e
in
d
iv
id
u
al
AR
M
A
a
n
d
T
DNN
s
ep
ar
atel
y
.
M
u
s
ta
f
i
[
1
9
]
also
d
ev
elo
p
ed
a
h
y
b
r
id
m
o
d
el
b
y
co
m
b
in
i
n
g
S
i
m
u
lated
A
n
n
ea
li
n
g
(
S
A
)
a
n
d
g
en
etic
p
r
o
g
r
a
m
m
in
g
,
t
h
e
d
e
v
elo
p
ed
m
o
d
el
p
r
o
d
u
ce
d
a
v
er
y
ac
cu
r
ate
f
o
r
s
o
lar
r
ad
iatio
n
p
r
ed
ictio
n
.
Mo
h
a
m
m
ad
i
et
al
[
2
0
]
p
r
o
p
o
s
e
d
a
h
y
b
r
id
S
u
p
p
o
r
t
Vec
to
r
Ma
ch
i
n
e
-
Fire
f
l
y
Alg
o
r
it
h
m
(
SVM
-
FFA
)
m
o
d
el
a
n
d
a
h
y
b
r
id
SVM
-
W
T
m
o
d
el
f
o
r
s
o
lar
r
ad
iatio
n
p
r
ed
ictio
n
.
T
h
e
t
w
o
m
o
d
els
d
ev
elo
p
ed
tu
r
n
ed
o
u
t
to
g
iv
e
ac
c
u
r
ate
esti
m
atio
n
an
d
w
h
en
co
m
p
ar
e
d
,
th
e
SVM
-
W
T
o
u
tp
er
f
o
r
m
s
th
e
SVM
-
FF
A
i
n
ter
m
s
o
f
ac
cu
r
ac
y
.
Olato
m
i
w
a
et
al
[
1
4
]
d
ev
elo
p
ed
a
h
y
b
r
id
SVM
-
FF
A
m
o
d
el
f
o
r
s
o
lar
r
ad
iatio
n
p
r
ed
ic
tio
n
i
n
Nig
er
ia
a
n
d
co
m
p
ar
ed
w
it
h
A
r
ti
f
icial
Neu
r
al
Ne
t
w
o
r
k
(
ANN
)
an
d
Gen
et
ic
P
r
o
g
r
a
m
m
in
g
(
GP
)
m
o
d
els.
T
h
e
SVM
-
FF
A
m
o
d
el
ac
cu
r
ac
y
o
u
tp
er
f
o
r
m
s
t
h
e
A
NN
a
n
d
GA
m
o
d
els.
I
n
th
i
s
s
t
u
d
y
,
t
h
e
e
f
f
icien
c
y
o
f
t
w
o
h
y
b
r
id
m
eth
o
d
s
n
a
m
el
y
P
ar
ticle
S
w
ar
m
Op
ti
m
i
za
tio
n
an
d
A
d
ap
tiv
e
Ne
u
r
o
-
f
u
zz
y
I
n
f
er
e
n
ce
S
y
s
te
m
(
P
SO
-
A
N
FIS
)
an
d
W
av
elet
T
r
an
s
f
o
r
m
an
d
A
d
ap
tiv
e
Ne
u
r
o
-
f
u
zz
y
I
n
f
er
e
n
ce
S
y
s
te
m
(
WT
-
A
N
FIS
)
w
er
e
ex
a
m
i
n
ed
f
o
r
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
in
Ni
g
er
i
a.
T
h
e
m
o
d
els
w
er
e
d
ev
elo
p
ed
b
y
co
m
b
in
in
g
A
d
ap
tiv
e
Ne
u
r
o
-
f
u
zz
y
I
n
f
er
en
ce
S
y
s
te
m
(
A
NFI
S)
w
it
h
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
P
SO)
an
d
W
a
v
elet
T
r
an
s
f
o
r
m
(
W
T
)
alg
o
r
i
th
m
s
to
f
o
r
ec
ast
th
e
h
o
r
izo
n
tal
s
o
lar
r
ad
iatio
n
.
A
N
FIS
is
a
r
o
b
u
s
t
h
y
b
r
id
in
te
llig
e
n
t
s
y
s
te
m
th
a
t
in
co
r
p
o
r
ates
th
e
lear
n
i
n
g
r
u
le
o
f
t
h
e
n
e
u
r
al
n
et
w
o
r
k
an
d
th
e
ex
h
ib
it
io
n
o
f
f
u
zz
y
lo
g
ic.
P
SO
an
d
W
T
ar
e
u
s
ed
to
o
p
tim
i
ze
th
e
ANFI
S
in
o
r
d
er
to
in
cr
ea
s
e
its
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
.
WT
is
a
s
ig
n
al
p
r
o
ce
s
s
in
g
to
o
l
u
s
ed
in
d
ec
o
m
p
o
s
in
g
a
n
d
r
ec
o
n
s
tr
u
cti
n
g
s
i
g
n
al
s
o
r
d
ata
in
to
d
if
f
er
e
n
t
f
r
eq
u
e
n
c
y
co
m
p
o
n
e
n
ts
[
2
1
]
.
P
SO
is
a
s
w
ar
m
i
n
tell
i
g
en
ce
o
p
ti
m
izatio
n
al
g
o
r
ith
m
in
s
p
ir
ed
as
a
r
esu
lt
o
f
th
e
b
eh
a
v
io
r
o
f
b
ir
d
s
an
d
f
i
s
h
e
s
w
h
ic
h
is
b
ased
o
n
t
h
eir
s
o
cial
in
ter
ac
tio
n
s
[
2
1
]
.
T
h
e
o
b
j
ec
tiv
e
o
f
th
i
s
s
t
u
d
y
is
to
i
n
v
esti
g
ate
t
h
e
f
o
r
ec
asti
n
g
ab
ilit
y
o
f
t
h
e
t
w
o
h
y
b
r
id
m
o
d
el
s
(
W
T
-
ANFI
S
a
n
d
P
S
O
-
A
N
FIS)
f
o
r
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
a
n
d
r
ec
o
g
n
ize
w
h
ich
o
f
th
e
t
w
o
m
o
d
el
s
h
a
s
a
b
etter
o
p
ti
m
izi
n
g
ab
ilit
y
f
o
r
th
e
p
r
ed
ictio
n
.
Sev
er
al
s
t
u
d
ies
h
a
v
e
b
ee
n
c
ar
r
ied
o
u
t
f
o
r
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
b
u
t
n
o
n
e
h
as
a
p
p
lied
W
T
-
A
N
FIS
ap
p
r
o
ac
h
.
T
h
e
f
o
r
ec
asti
n
g
is
d
o
n
e
u
s
i
n
g
t
h
e
m
eteo
r
o
lo
g
ica
l
d
ata
av
ailab
le
at
th
e
ca
s
e
s
tu
d
y
w
h
ich
i
n
cl
u
d
es
s
u
n
s
h
i
n
e
h
o
u
r
s
,
r
elati
v
e
h
u
m
i
d
it
y
,
m
in
i
m
u
m
te
m
p
er
atu
r
e,
a
n
d
m
a
x
i
m
u
m
te
m
p
er
at
u
r
e.
W
h
ich
ar
e
co
n
s
id
er
e
d
as in
p
u
ts
to
th
e
d
ev
e
lo
p
ed
h
y
b
r
id
m
o
d
els.
2.
M
AT
E
RIAL
S AN
D
M
E
T
H
O
DS
2
.
1
.
Da
t
a
co
llect
io
n
Me
teo
r
o
lo
g
ical
d
ata
f
o
r
1
0
y
ea
r
s
p
er
io
d
r
an
g
in
g
f
r
o
m
2
0
0
2
-
2
0
1
2
w
er
e
co
llected
f
r
o
m
th
e
Nig
er
ia
n
Me
teo
r
o
lo
g
ical
Ag
e
n
c
y
(
NI
ME
T
)
an
d
ar
e
u
s
ed
to
ca
r
r
y
o
u
t
th
i
s
s
t
u
d
y
.
Mo
n
t
h
l
y
a
v
er
a
g
e
s
o
lar
r
ad
iatio
n
d
ata
w
er
e
u
s
ed
as
th
e
o
u
tp
u
t.
Oth
e
r
m
eteo
r
o
lo
g
ical
d
ata
u
s
ed
as
th
e
in
p
u
t
in
cl
u
d
es
t
h
e
m
o
n
th
l
y
a
v
er
ag
e
s
u
n
s
h
i
n
e
h
o
u
r
s
(
SH)
,
r
elati
v
e
h
u
m
id
it
y
(
R
H)
,
m
i
n
i
m
u
m
te
m
p
er
atu
r
e
(
T
m
in
)
an
d
m
a
x
i
m
u
m
te
m
p
er
at
u
r
e
(
T
m
ax
)
.
T
h
e
m
eteo
r
o
lo
g
ical
d
ata
co
ll
ec
ted
w
er
e
f
o
r
Kan
o
s
ta
te
Nig
er
ia
w
i
th
lo
n
g
itu
d
e
1
2
.
0
0
2
2
º
N
an
d
latitu
d
e
8
.
9
5
2
º
E
,
th
e
m
o
n
t
h
l
y
a
v
er
ag
e
o
f
t
h
e
m
eteo
r
o
lo
g
ical
d
ata
u
s
ed
ar
e
p
r
esen
ted
i
n
Fi
g
u
r
e
1
.
T
h
e
d
ata
o
b
tain
e
d
w
er
e
d
i
v
id
ed
in
to
t
w
o
(
tr
ain
i
n
g
d
ata
a
n
d
test
i
n
g
d
ata)
,
th
e
tr
ain
i
n
g
d
ata
s
et
s
r
an
g
e
f
r
o
m
2
0
0
2
-
2
0
0
9
(
7
0
%)
an
d
th
e
test
in
g
d
ata
s
et
r
an
g
es
f
r
o
m
2
0
1
0
-
2
0
1
2
(
3
0
%).
Fig
u
r
e
1
.
A
t
y
p
ica
l
A
NFI
S s
tr
u
ctu
r
e
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.
9
,
No
.
5
,
Octo
b
er
2
0
1
9
:
3
9
1
6
-
3926
3918
2
.
2
.
Ada
ptiv
e
neuro
-
f
uzzy
infe
re
nce
s
y
s
t
e
m
(
ANF
I
S)
A
N
FIS
w
as
f
ir
s
t
d
ev
elo
p
ed
in
th
e
y
ea
r
1
9
9
3
b
y
J
.
S
R
o
g
er
th
r
o
u
g
h
t
h
e
co
m
b
i
n
atio
n
o
f
n
eu
r
a
l
n
et
w
o
r
k
a
n
d
f
u
zz
y
r
ea
s
o
n
i
n
g
[
2
2
]
.
T
h
e
A
NFI
S
p
r
o
v
id
es
a
b
asic
s
et
o
f
S
u
g
e
n
o
-
t
y
p
e
“Í
F….T
HE
N”
f
u
zz
y
in
f
er
en
ce
s
y
s
te
m
w
it
h
a
n
e
u
r
al
n
et
w
o
r
k
as
t
h
e
f
u
zz
y
r
u
le
en
g
i
n
e.
T
h
e
A
N
FIS
is
a
n
et
wo
r
k
s
tr
u
ct
u
r
e
th
at
is
r
eg
ar
d
ed
as
m
o
r
e
ef
f
ec
ti
v
e
th
an
th
e
i
n
d
iv
id
u
al
f
u
zz
y
s
y
s
te
m
s
o
r
n
eu
r
al
n
et
w
o
r
k
.
I
t
d
eliv
er
s
a
m
o
r
e
o
p
ti
m
u
m
r
esu
lt
th
a
n
an
y
o
f
th
e
t
w
o
s
y
s
t
e
m
s
[
2
3
]
.
A
n
ANFI
S
s
tr
u
ctu
r
e
w
it
h
in
p
u
t
s
an
d
an
d
o
u
p
u
t
is
p
r
esen
ted
in
F
ig
u
r
e
1
.
T
h
e
A
NFI
S
s
tr
u
ct
u
r
e
co
n
s
is
t
o
f
5
la
y
er
s
w
i
th
ea
c
h
o
f
th
e
la
y
er
s
h
av
i
n
g
d
is
s
i
m
ila
r
f
u
n
ctio
n
s
.
I
n
t
h
is
s
tu
d
y
,
t
h
e
A
NFI
S
u
s
ed
h
as
f
o
u
r
in
p
u
t
s
an
d
o
n
e
o
u
tp
u
t,
w
it
h
th
e
f
iv
e
la
y
er
s
co
m
p
r
is
i
n
g
o
f
n
o
d
es
in
ea
ch
o
f
th
e
la
y
er
s
,
an
d
t
h
e
n
o
d
es o
n
t
h
ese
la
y
er
s
ca
r
r
y
o
u
t t
h
e
s
a
m
e
f
u
n
c
tio
n
.
I
f
is
A
1
an
d
is
B
1
,
th
en
,
(
1
)
I
f
is
A
2
an
d
is
B
2
,
th
en
,
(
2
)
w
h
er
e,
,
an
d
ar
e
p
r
ec
ed
in
g
p
ar
a
m
eter
s
.
L
a
y
er
1
:
I
t
co
m
p
r
is
es
o
f
an
in
p
u
t
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
s
w
h
ic
h
s
u
p
p
lies
t
h
e
in
p
u
ts
to
la
y
er
t
w
o
.
T
h
e
n
o
d
es
in
la
y
er
1
co
m
p
r
is
e
o
f
n
o
d
e
f
u
n
c
tio
n
s
r
ef
er
r
ed
to
as
ad
ap
tiv
e
n
o
d
es.
Ou
tp
u
ts
o
f
th
e
s
e
n
o
d
es
ar
e
p
r
esen
ted
in
(
3
)
an
d
(
4
)
r
esp
ec
tiv
el
y
.
(
)
f
o
r
=
1
,
2
(
3)
o
r
(
)
f
o
r
=
3
,
4
(
4
)
(
)
an
d
(
)
r
ep
r
esen
ts
t
h
e
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
s
o
f
n
o
d
e
A
,
x
o
r
y
is
t
h
e
in
p
u
t
o
f
n
o
d
e
i
,
a
n
d
o
r
is
a
co
n
n
ec
ted
lin
g
u
is
tic
lab
el.
is
th
e
m
e
m
b
er
s
h
ip
r
atin
g
o
f
f
u
zz
y
s
et
s
A
an
d
B
.
I
n
(
5
)
p
r
esen
ts
t
h
e
g
lo
b
al
f
u
n
ct
io
n
o
f
t
h
e
n
o
n
-
lin
e
ar
co
n
s
tr
ain
t
s
(
5
)
[
2
4
]
an
d
[
13
]
.
(
)
(
)
(
5
)
w
h
er
e
ar
e
th
e
s
et
s
o
f
p
ar
a
m
e
ter
s
.
T
h
e
f
u
n
ctio
n
v
ar
ie
s
as
th
e
p
ar
am
eter
s
ch
a
n
g
e,
th
er
eb
y
ex
h
ib
it
in
g
d
iv
er
s
e
f
o
r
m
s
o
f
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
t
y
p
e
f
o
r
f
u
zz
y
s
et
A
.
L
a
y
er
2
:
T
h
e
in
co
m
in
g
s
i
g
n
als
f
r
o
m
t
h
e
f
ir
s
t
la
y
er
ar
e
m
u
ltip
lied
in
t
h
is
la
y
er
an
d
th
e
o
b
tain
ed
r
esu
lt
s
ar
e
s
en
t
o
u
t
as
t
h
e
o
u
t
p
u
t.
T
h
e
o
u
tp
u
t
is
co
n
s
id
er
ed
as
an
A
ND
o
r
OR
p
r
o
ce
d
u
r
e
o
f
th
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
w
h
ich
co
m
es
f
r
o
m
th
e
p
r
ec
ed
in
g
la
y
er
[
2
5
]
.
I
t is p
r
esen
ted
i
n
(
6
)
.
(
6
)
w
h
er
e
in
d
icate
s
t
h
e
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
o
f
n
o
d
e
A
a
n
d
is
t
h
e
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
o
f
n
o
d
e
B
L
a
y
er
3
:
t
h
is
is
a
n
o
n
-
ad
ap
tiv
e
la
y
er
w
h
ic
h
i
s
a
ls
o
ca
lled
th
e
n
o
r
m
al
izatio
n
la
y
er
.
T
h
e
t
h
ir
d
la
y
er
u
s
u
all
y
s
et
t
h
e
r
u
le
s
[
2
6
]
.
I
t is
th
e
r
atio
o
f
n
o
d
e
i
f
ir
i
n
g
s
tr
en
g
th
to
th
e
s
u
m
o
f
all
r
u
les
f
ir
in
g
s
tr
en
g
th
s
̅
(
7
)
I
n
d
icate
s
th
e
f
ir
i
n
g
s
tr
en
g
t
h
L
a
y
er
4
:
lay
er
f
o
u
r
co
n
s
is
t
s
o
f
n
o
d
es
th
at
ar
e
all
ad
ap
tiv
e
n
o
d
es
w
it
h
ea
ch
o
f
t
h
e
n
o
d
es
co
m
p
u
ti
n
g
th
e
i
th
co
n
tr
ib
u
tio
n
to
t
h
e
o
u
t
p
u
t.
I
t
is
th
e
p
r
o
d
u
ct
o
f
t
h
e
s
i
g
n
al
co
n
tr
o
lled
b
y
th
e
p
r
e
v
io
u
s
n
o
d
e
w
h
ic
h
g
iv
e
s
n
o
d
e
i
[2
6
,
2
7
]
.
̅
̅
̅
̅
̅
̅
̅
̅
(
)
(
8
)
̅
̅
̅
is
th
e
n
o
r
m
alize
d
f
ir
in
g
s
tr
en
g
t
h
f
r
o
m
t
h
e
p
r
ev
io
u
s
la
y
er
,
w
h
ile
ar
e
th
e
p
r
ec
e
d
in
g
p
ar
am
eter
s
.
L
a
y
er
5
:
T
h
is
b
ein
g
t
h
e
last
la
y
er
co
m
p
r
is
es
o
f
a
s
in
g
le
n
o
d
e
w
h
ic
h
co
m
p
u
tes
t
h
e
o
u
tp
u
t
s
.
T
h
e
o
u
tp
u
t is th
e
s
u
m
m
atio
n
o
f
all
t
h
e
in
co
m
i
n
g
s
ig
n
al
s
co
m
in
g
f
r
o
m
t
h
e
p
r
ev
io
u
s
la
y
er
[
2
2
,
2
5
]
.
∑
̅
̅
̅
(
9
)
w
h
er
e
is
t
h
e
s
u
m
m
atio
n
o
f
t
h
e
in
co
m
i
n
g
s
i
g
n
a
ls
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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3919
2
.
3
.
P
a
rt
icle
s
w
a
rm
o
pti
m
iza
t
io
n
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
P
SO)
is
a
s
w
ar
m
in
telli
g
en
ce
o
p
tim
izat
io
n
alg
o
r
it
h
m
d
e
v
elo
p
ed
b
y
E
b
er
h
ar
t a
n
d
Ke
n
n
ed
y
in
t
h
e
y
ea
r
1
9
9
5
[
2
8
]
.
T
h
e
alg
o
r
ith
m
is
in
s
p
ir
ed
as a
r
e
s
u
lt o
f
t
h
e
b
eh
av
io
r
o
f
b
ir
d
s
a
n
d
f
is
h
es
w
h
ic
h
is
b
ased
o
n
th
eir
s
o
cial
in
ter
ac
tio
n
s
[
2
1
]
.
A
s
th
ese
b
ir
d
s
an
d
f
i
s
h
e
s
g
o
r
an
d
o
m
l
y
i
n
s
ea
r
ch
f
o
r
f
o
o
d
,
ea
ch
o
f
th
e
s
e
b
ir
d
s
o
r
f
i
s
h
s
er
v
e
as
a
s
in
g
le
s
o
lu
tio
n
,
th
ese
s
o
lu
tio
n
s
ca
n
b
e
ex
p
lai
n
ed
as
p
ar
ticles
in
a
s
w
ar
m
.
I
n
P
SO,
ea
ch
o
f
t
h
ese
p
ar
ticles
g
o
es
i
n
s
ea
r
c
h
o
f
a
p
o
s
s
ib
le
s
o
lu
t
io
n
to
a
g
i
v
en
p
r
o
b
lem
.
T
h
e
m
o
v
e
m
en
t
o
f
t
h
ese
p
ar
tic
les
is
c
h
ar
ac
ter
ized
b
y
w
h
er
e
th
e
y
f
i
t
b
est
a
n
d
th
eir
c
u
r
r
en
t
lo
ca
tio
n
s
w
it
h
t
h
e
n
u
m
b
er
s
o
f
t
h
e
s
w
ar
m
[
2
9
]
.
P
SO
is
r
eg
ar
d
ed
as
a
p
o
p
u
latio
n
-
b
ase
d
ex
p
lo
r
atio
n
m
et
h
o
d
w
it
h
ea
c
h
i
m
p
e
n
d
i
n
g
s
o
lu
tio
n
(
a
s
w
ar
m
)
r
ep
r
esen
ts
a
p
ar
ticle
o
f
a
p
o
p
u
latio
n
.
T
h
e
p
ar
ticles
co
n
ti
n
u
e
to
ch
an
g
e
t
h
eir
p
o
s
itio
n
s
d
u
r
in
g
th
e
ir
r
an
d
o
m
s
ea
r
ch
u
n
ti
l
th
e
y
attai
n
a
n
o
p
ti
m
al
s
tate.
P
SO
as
an
o
p
ti
m
izatio
n
alg
o
r
it
h
m
h
a
s
b
ee
n
u
ti
lized
to
s
o
l
v
e
n
u
m
er
o
u
s
o
p
ti
m
izat
io
n
p
r
o
b
lem
s
a
n
d
h
a
s
p
r
o
v
ed
its
ef
f
ec
ti
v
en
es
s
an
d
e
f
f
ic
ien
c
y
as
a
u
s
e
f
u
l
to
o
l
f
o
r
s
o
lv
in
g
o
p
tim
izatio
n
p
r
o
b
le
m
s
[
3
0
]
.
T
h
e
ef
f
ec
ti
v
e
n
ess
o
f
P
SO
w
a
s
s
h
o
w
n
i
n
p
r
ev
io
u
s
e
m
p
ir
ical
s
t
u
d
ies
[
3
1
]
.
I
n
P
SO,
ea
ch
p
ar
ticle
is
a
s
s
o
ciate
d
w
it
h
i
ts
b
est
s
o
lu
t
io
n
(
p
b
est
)
o
f
its
co
o
r
d
in
ate
i
n
th
e
p
r
o
b
lem
s
p
ac
e.
T
h
en
f
o
llo
w
ed
b
y
a
n
o
th
er
b
est
v
al
u
e
(
ib
es
t
)
w
h
ic
h
is
o
b
tain
ed
b
y
a
n
y
p
ar
ticle
n
ex
t
to
t
h
e
p
ar
ticle.
W
h
en
a
p
ar
ticle
tak
es
all
th
e
p
o
p
u
latio
n
as
th
e
to
p
o
lo
g
ical
n
eig
h
b
o
r
s
,
th
e
b
est
v
al
u
e
is
ca
ll
ed
(
g
b
est
)
w
h
ic
h
is
th
e
g
lo
b
al
b
est
v
al
u
e.
T
h
e
v
e
lo
cit
y
v
ec
to
r
is
u
p
d
ati
n
g
ac
co
r
d
in
g
to
th
e
p
o
s
itio
n
o
f
g
b
est
an
d
p
b
est
.
In
(
1
0
)
an
d
(
1
1
)
illu
s
tr
ates
h
o
w
t
h
e
p
ar
ticle
p
o
s
itio
n
an
d
v
elo
cit
y
ar
e
u
p
d
ated
.
T
h
e
v
elo
cit
y
v
ec
to
r
is
u
p
d
atin
g
ac
co
r
d
in
g
to
th
e
p
o
s
itio
n
o
f
an
d
.
(
)
(
)
(
(
)
)
(
(
)
)
(
1
0
)
(
)
(
)
(
)
(
1
1
)
w
h
er
e
(
)
is
th
e
ag
en
t
v
e
lo
cit
y
at
iter
atio
n
,
is
th
e
in
er
tia
w
eig
h
t,
w
is
th
e
w
ei
g
h
in
g
f
ac
to
r
o
f
in
er
tia,
ar
e
r
an
d
o
m
v
ar
iab
les
an
d
w
it
h
(
)
,
an
d
an
d
ar
e
p
o
s
itiv
e
ac
ce
ler
atio
n
co
n
s
tan
ts
.
Fig
u
r
e
2
d
e
m
o
n
s
tr
ate
t
h
e
P
SO
s
ea
r
c
h
m
ec
h
a
n
i
s
m
u
s
i
n
g
v
elo
cit
y
u
p
d
ate
r
u
le
(
1
0
)
an
d
p
o
s
itio
n
u
p
d
ate
(
1
1
)
.
Fig
u
r
e
2
.
Up
d
atin
g
t
h
e
p
o
s
itio
n
in
g
m
ec
h
a
n
i
s
m
o
f
P
SO
2
.
4
.
Wa
v
elet
t
ra
ns
f
o
rm
W
av
elet
tr
an
s
f
o
r
m
(
W
T
)
is
a
s
ig
n
a
l
p
r
o
ce
s
s
i
n
g
to
o
l
s
i
m
ilar
t
o
Fo
u
r
ier
T
r
an
s
f
o
r
m
u
s
ed
to
d
ec
o
m
p
o
s
e
ti
m
e
s
er
ie
s
s
i
g
n
al
i
n
to
d
if
f
er
en
t
f
r
eq
u
e
n
c
y
co
m
p
o
n
e
n
ts
.
W
T
h
as
b
ee
n
w
id
el
y
u
s
ed
in
b
o
th
s
cien
ti
f
ic
an
d
en
g
i
n
ee
r
i
n
g
ap
p
licatio
n
s
[
3
2
,
3
3
]
,
p
ar
ticu
lar
l
y
in
th
e
ar
ea
s
o
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et
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ies
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ata
[
17
,
3
4
]
.
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h
e
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m
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ata
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e
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w
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ich
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ter
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t
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Fig
u
r
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3
,
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is
al
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o
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ep
en
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e
a
p
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.
Fig
u
r
e
4
p
r
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th
e
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o
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o
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itio
n
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d
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s
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w
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ated
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e
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
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8
8
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8708
I
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t J
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lec
&
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p
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g
,
Vo
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9
,
No
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5
,
Octo
b
er
2
0
1
9
:
3
9
1
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-
3926
3920
Fig
u
r
e
3
.
Fo
u
r
Mo
th
er
w
a
v
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ts
f
u
n
ctio
n
s
(
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(
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(
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(
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3
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w
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o
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er
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le
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ac
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e
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ar
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h
e
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o
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o
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t
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e
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ata
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n
d
u
cted
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s
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g
(
1
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)
w
ith
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e
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ar
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m
eter
s
m
ai
n
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n
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g
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o
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ig
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n
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ef
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it
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Fig
u
r
e
4
.
T
w
o
lev
el
s
w
av
e
let
d
ec
o
m
p
o
s
itio
n
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d
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ec
o
n
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tr
u
ct
io
n
d
iag
r
a
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s
3.
M
O
DE
L
DE
VE
L
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P
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E
NT
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h
e
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ce
d
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r
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ed
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n
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ev
e
l
o
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e
t
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els
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e
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Fi
g
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r
es
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d
6
.
T
h
e
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y
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ap
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r
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h
in
th
is
s
t
u
d
y
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t
h
e
co
m
b
i
n
atio
n
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f
A
NFI
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d
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an
d
A
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S
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n
d
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T
.
T
h
e
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r
e
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ictio
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er
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ed
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s
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w
h
ile
th
e
P
SO a
n
d
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T
ar
e
u
s
ed
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m
p
r
o
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e
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er
f
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r
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ce
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NFI
S
m
o
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e
l.
3
.
1
.
WT
-
ANF
I
S d
ev
elo
p
m
e
nt
W
T
is
ap
p
lied
to
th
e
ti
m
e
s
er
ies
d
ata
u
s
ed
f
o
r
t
h
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S
p
r
ed
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n
.
T
h
e
o
r
ig
in
al
d
ata
o
b
tain
ed
is
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ir
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t
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ec
o
m
p
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to
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els
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ar
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t
m
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al
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n
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s
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Db
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e
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e
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ile
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n
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ig
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ter
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o
m
p
o
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itio
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t
h
e
p
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ce
s
s
o
f
r
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o
n
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tr
u
c
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n
o
f
t
h
e
s
ig
n
al
s
is
th
en
ca
r
r
ied
o
u
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:
2
0
8
8
-
8708
S
o
la
r
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a
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ia
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F
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F
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a
p
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ch
(
S
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n
i S
a
li
s
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)
3921
as
ill
u
s
tr
ated
i
n
Fi
g
u
r
e
5
.
T
h
e
o
u
tp
u
t
f
r
o
m
th
e
d
e
v
elo
p
ed
W
T
-
A
NFI
S
m
o
d
el
p
r
esen
ts
th
e
p
r
ed
icted
s
o
lar
r
ad
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n
at
th
e
e
n
d
o
f
t
h
e
s
i
m
u
latio
n
p
r
o
ce
s
s
.
In
t
h
e
b
eg
i
n
n
i
n
g
,
t
h
e
d
ata
is
f
ir
s
t
ar
r
an
g
ed
in
a
m
atr
i
x
f
o
r
m
an
d
p
r
esen
ted
o
n
an
ex
ce
l
s
h
ee
t,
f
o
r
th
is
s
t
u
d
y
co
m
p
r
is
i
n
g
o
f
f
o
u
r
in
p
u
t
s
an
d
o
n
e
o
u
tp
u
t,
th
e
f
ir
s
t
f
o
u
r
co
lu
m
n
s
o
n
th
e
ex
ce
l
s
h
ee
t
r
ep
r
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t
th
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i
n
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u
t
s
w
h
i
le
th
e
f
i
f
th
co
l
u
m
n
r
ep
r
esen
ts
t
h
e
o
u
tp
u
t
d
ata.
T
h
e
in
p
u
t
d
ata
co
lu
m
n
s
ar
e
r
eg
ar
d
ed
as
th
e
r
ea
l
in
p
u
t
s
.
W
T
is
f
ir
s
t
u
s
ed
to
d
ec
o
m
p
o
s
e
th
e
d
ata
u
s
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n
g
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h
e
t
w
o
-
le
v
el
m
o
t
h
er
w
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v
elet
Db
2
b
ef
o
r
e
th
e
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r
ed
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s
tar
ts
.
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s
m
en
tio
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r
lier
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e
Db
2
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ch
o
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en
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ec
a
u
s
e
it
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i
d
ely
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g
r
ee
d
th
at
it
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s
ca
p
ab
le
o
f
p
r
o
v
id
in
g
a
w
o
r
t
h
y
es
ti
m
a
tio
n
o
f
t
h
e
s
ig
n
al
s
.
T
w
o
ap
p
r
o
x
im
ate
a
n
d
d
etailed
co
ef
f
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n
ts
ar
e
ch
o
s
en
f
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ec
o
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ed
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au
s
e
t
h
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y
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m
o
r
e
in
f
o
r
m
a
tio
n
o
n
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o
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t
th
e
d
ata
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s
ed
.
Fo
r
t
h
is
r
ea
s
o
n
,
ea
ch
o
f
th
e
t
w
o
co
ef
f
icie
n
t
s
i
g
n
a
ls
w
ill
b
e
tr
ain
ed
u
s
i
n
g
a
d
if
f
er
en
t
A
N
FIS
n
et
w
o
r
k
.
T
h
e
w
a
n
ted
s
ig
n
al
s
ar
e
th
e
n
f
o
r
w
ar
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ed
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e
A
NFI
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tr
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ct
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r
e
an
d
t
h
e
A
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i
s
t
h
en
tr
ai
n
ed
u
s
in
g
t
h
e
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ata,
b
y
ad
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u
s
ti
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g
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N
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ar
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r
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ai
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ase
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n
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r
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er
to
s
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tis
f
y
t
h
e
s
u
b
m
it
ted
o
u
tp
u
ts
.
T
h
is
s
a
m
e
p
r
o
ce
s
s
is
r
ep
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ted
in
th
e
te
s
ti
n
g
s
ta
g
e
u
s
in
g
t
h
e
u
n
u
s
ed
d
ata
f
r
o
m
th
e
tr
ai
n
i
n
g
s
tag
e.
T
h
e
o
u
tp
u
t
s
f
r
o
m
t
h
e
ANFI
S
w
ill
b
e
ex
tr
ac
ted
an
d
t
h
e
W
T
w
il
l
t
h
en
b
e
u
s
ed
f
o
r
th
e
r
ec
o
n
s
tr
u
ctio
n
o
f
t
h
o
s
e
o
u
tp
u
ts
,
th
e
r
ec
o
n
s
tr
u
cted
o
u
tp
u
t
s
f
r
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m
th
e
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T
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r
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v
id
e
th
e
f
in
al
s
o
lar
r
ad
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n
p
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ed
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n
o
u
tp
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t
b
y
W
T
-
A
NFI
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ap
p
r
o
ac
h
.
T
h
e
er
r
o
r
m
ar
g
i
n
b
et
w
ee
n
th
e
tar
g
et
o
u
tp
u
t
a
n
d
th
e
p
r
ed
icted
o
u
tp
u
t
i
s
t
h
e
n
c
o
m
p
u
ted
u
s
i
n
g
t
h
e
s
tatis
t
ical
in
d
icato
r
s
.
R
MSE
a
n
d
R
²
ar
e
u
s
ed
f
o
r
p
er
f
o
r
m
a
n
c
e
ev
alu
at
io
n
o
f
t
h
e
d
ev
elo
p
ed
W
T
-
A
NFI
S.
S
E
T
I
N
P
U
T
P
A
R
A
M
E
T
E
R
C
O
N
V
E
R
G
E
N
C
E
?
NO
A
N
F
I
S
T
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A
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N
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S
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A
R
T
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T
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T
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N
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W
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M
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I
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Fig
u
r
e
5
.
WT
-
A
N
FIS
m
o
d
el
f
l
o
w
c
h
ar
t
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Y
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R
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I
N
D
A
T
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T
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G
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N
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IN
F
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R
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T
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F
D
A
T
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P
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O
Fig
u
r
e
6
.
P
SO
-
A
NFI
S
m
o
d
el
f
lo
w
c
h
ar
t
3
.
2
.
P
SO
-
ANF
I
S d
ev
elo
p
m
e
nt
T
h
e
d
ata
s
et
u
s
ed
f
o
r
th
is
s
tu
d
y
ar
e
f
ir
s
t
p
r
ese
n
ted
in
a
m
atr
i
x
f
o
r
m
o
n
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n
e
x
ce
l
s
h
ee
t
s
i
m
i
lar
to
th
at
o
f
t
h
e
W
T
,
th
ese
d
ata
s
et
ar
e
p
r
esen
ted
i
n
co
l
u
m
n
s
w
it
h
4
co
lu
m
n
s
as
i
n
p
u
ts
an
d
1
co
lu
m
n
a
s
o
u
tp
u
t.
T
h
e
n
u
m
b
er
o
f
co
lu
m
n
s
o
f
th
e
in
p
u
t
d
ata
r
ep
r
esen
ts
t
h
e
r
ea
l
in
p
u
t
s
.
T
h
e
A
NFI
S
is
t
h
en
tr
ai
n
ed
u
s
i
n
g
th
e
d
ata
s
et
p
r
esen
ted
.
T
h
is
tr
ain
in
g
is
d
o
n
e
u
s
i
n
g
th
e
d
e
f
au
l
t
tr
ain
i
n
g
alg
o
r
it
h
m
in
ANFI
S
w
h
ic
h
ar
e
th
e
least
s
q
u
ar
e
esti
m
atio
n
(
L
SE)
in
t
h
e
f
o
r
w
a
r
d
p
ass
an
d
Gr
ad
ien
t
Desce
n
t
(
GD)
in
t
h
e
b
ac
k
w
ar
d
p
ass
.
T
h
e
L
SE
is
u
s
ed
to
d
eter
m
in
e
t
h
e
co
n
s
eq
u
en
t
p
ar
a
m
eter
s
(
)
in
th
e
f
o
r
w
ar
d
p
ass
,
an
d
th
e
GD
is
u
s
ed
to
u
p
d
ate
th
e
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
p
ar
a
m
et
er
s
in
t
h
e
b
ac
k
w
ar
d
p
as
s
.
T
h
e
s
y
s
te
m
p
ar
a
m
eter
s
ar
e
ad
j
u
s
te
d
as
i
n
p
u
t
s
/o
u
tp
u
ts
d
u
r
in
g
th
e
tr
ain
i
n
g
p
r
o
ce
s
s
.
T
h
e
s
a
m
e
ap
p
lies
to
th
e
test
i
n
g
p
r
o
ce
s
s
f
o
r
th
e
d
ata
th
at
h
as
n
o
t
b
ee
n
u
s
ed
d
u
r
in
g
th
e
tr
ain
in
g
p
r
o
ce
s
s
.
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.
9
,
No
.
5
,
Octo
b
er
2
0
1
9
:
3
9
1
6
-
3926
3922
T
o
o
b
tain
m
o
r
e
ac
cu
r
ate
r
esu
lts
,
P
SO
is
u
s
ed
to
tr
ain
th
e
p
ar
am
eter
s
o
f
th
e
A
NFI
S
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
.
T
h
is
is
d
o
n
e
b
y
cr
ea
tin
g
an
N
-
d
i
m
e
n
s
io
n
v
ec
to
r
w
h
er
e
N
d
en
o
tes
t
h
e
n
u
m
b
er
o
f
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
.
T
h
e
P
SO
alg
o
r
ith
m
o
p
ti
m
ize
s
th
e
m
e
m
b
er
s
h
i
p
f
u
n
c
tio
n
p
ar
a
m
eter
s
co
n
tai
n
ed
in
th
e
v
ec
to
r
.
T
h
e
P
SO
alg
o
r
ith
m
p
ar
a
m
e
te
r
s
ar
e
th
en
d
e
f
in
ed
a
n
d
in
itia
li
ze
d
r
an
d
o
m
l
y
d
u
r
in
g
t
h
e
f
ir
s
t
s
ta
g
e.
T
h
e
P
SO
alg
o
r
ith
m
th
e
n
k
ee
p
s
u
p
d
ati
n
g
t
h
ese
p
ar
a
m
eter
s
b
y
u
p
d
ati
n
g
o
n
e
p
ar
a
m
eter
o
f
t
h
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
d
u
r
in
g
ea
ch
iter
atio
n
.
T
h
is
u
p
d
ate
co
n
tin
u
e
s
f
r
o
m
iter
atio
n
to
iter
atio
n
u
n
t
il
w
e
o
b
tain
th
e
b
es
t
p
o
s
s
ib
le
s
o
lu
tio
n
b
y
o
b
tain
in
g
a
m
i
n
i
m
u
m
er
r
o
r
s
et.
T
h
e
o
u
tp
u
t
o
f
th
e
A
NFI
S
is
t
h
en
e
x
tr
ac
ted
u
s
in
g
th
e
p
ar
a
m
eter
s
o
b
tain
ed
b
y
t
h
e
P
SO,
an
d
th
i
s
o
u
tp
u
t g
iv
e
s
th
e
f
o
r
ec
asted
o
u
t
p
u
t o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
P
SO
-
A
N
FIS
m
o
d
el.
3
.
3
.
M
o
del per
f
o
r
m
a
nce
a
s
s
e
s
s
ment
T
h
e
h
y
b
r
id
P
SO
-
A
N
FIS
an
d
W
T
-
A
N
FIS
m
o
d
els
p
er
f
o
r
m
an
ce
ar
e
an
al
y
ze
d
u
s
i
n
g
th
e
f
o
llo
w
in
g
s
tatis
t
ical
in
d
icato
r
s
R
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
√
∑
(
̅
)
(
1
5
)
T
h
e
co
ef
f
icie
n
t
o
f
d
eter
m
i
n
ati
o
n
(
)
∑
(
̅
̅
̅
)
(
)
̅
̅
̅
̅
∑
(
̅
̅
̅
)
∑
(
̅
)
(
1
6
)
w
h
er
e
ar
e
th
e
p
r
ed
icted
a
n
d
tar
g
et
v
alu
e
s
,
w
h
ile
̅
̅
r
e
p
r
esen
ts
th
e
m
ea
n
v
a
lu
e
s
o
f
A
l
s
o
,
n
d
en
o
tes
t
h
e
e
n
tire
tes
t
d
ata
a
m
o
u
n
t.
L
o
w
er
v
a
lu
e
s
o
f
R
MSE
a
n
d
M
A
P
E
s
ig
n
i
f
i
es
g
o
o
d
p
er
f
o
r
m
a
n
ce
w
h
ile
h
ig
h
er
v
al
u
es o
f
s
ig
n
i
f
ies
g
o
o
d
m
o
d
el
p
er
f
o
r
m
an
ce
w
h
ile
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
NS
I
n
t
h
is
s
t
u
d
y
,
th
e
ef
f
icie
n
c
y
o
f
t
w
o
h
y
b
r
id
m
et
h
o
d
s
n
a
m
el
y
P
SO
-
A
N
FIS
a
n
d
W
T
-
ANFI
S
w
er
e
ex
a
m
in
ed
f
o
r
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
i
n
Nig
er
ia.
T
h
e
tw
o
m
o
d
els
w
er
e
d
ev
elo
p
ed
s
ep
ar
ately
,
o
n
e
is
b
y
o
p
tim
izin
g
t
h
e
ANFI
S
w
i
th
P
SO
an
d
t
h
e
o
t
h
er
is
b
y
d
ec
o
m
p
o
s
in
g
th
e
m
o
d
el
i
n
p
u
ts
u
s
i
n
g
W
T
.
T
h
e
ac
cu
r
ac
y
o
f
t
h
e
t
w
o
h
y
b
r
id
m
o
d
els
w
as
ass
e
s
s
ed
u
s
i
n
g
R
MSE
an
d
R
²
.
Fro
m
t
h
e
o
b
tai
n
ed
v
alu
e
s
o
f
th
e
R
MSE
an
d
R
²,
it
w
a
s
clea
r
th
at
th
e
t
w
o
h
y
b
r
i
d
m
o
d
els
w
er
e
b
o
th
v
iab
le
f
o
r
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
.
T
h
e
o
b
tain
ed
r
esu
lt
s
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
es 7
an
d
8
.
Fig
u
r
e
7
p
r
esen
t
s
th
e
s
ca
tter
p
lo
ts
o
f
t
h
e
d
ev
elo
p
ed
h
y
b
r
id
m
o
d
el
s
,
f
r
o
m
Fi
g
u
r
es
7
(
a)
an
d
(
b
)
w
h
ic
h
is
t
h
e
s
ca
tter
p
lo
t
f
o
r
th
e
P
SO
-
A
NFI
S,
it
is
c
lear
th
a
t
t
h
er
e’
s
a
v
er
y
g
o
o
d
co
n
v
er
g
e
n
ce
b
e
t
w
ee
n
t
h
e
p
r
ed
icted
s
o
lar
r
ad
iatio
n
an
d
th
e
tar
g
et
s
o
lar
r
ad
iatio
n
b
o
th
at
th
e
tr
ain
in
g
a
n
d
test
i
n
g
p
h
a
s
e.
Fi
g
u
r
e
s
7
(
c)
an
d
(
d
)
also
p
r
esen
ts
t
h
e
s
ca
tter
p
lo
t
o
f
t
h
e
W
T
-
A
NFI
S
m
o
d
el,
w
h
ich
a
ls
o
s
h
o
w
s
a
clea
r
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
e
tar
g
e
t
an
d
t
h
e
f
o
r
ec
asted
o
u
tp
u
t
at
b
o
th
t
h
e
tr
ai
n
i
n
g
p
h
ase
an
d
th
e
test
i
n
g
p
h
a
s
e.
A
lt
h
o
u
g
h
t
h
e
t
w
o
m
o
d
els
s
h
o
w
a
v
er
y
g
o
o
d
co
r
r
elatio
n
b
et
w
e
en
t
h
e
f
o
r
ec
asted
o
u
tp
u
t
a
n
d
tar
g
et
o
u
tp
u
t,
t
h
e
W
T
-
ANFI
S
s
h
o
w
s
m
o
r
e
co
n
v
er
g
e
n
ce
w
h
en
co
m
p
ar
ed
w
it
h
P
SO
-
A
NFI
S
m
o
d
el,
th
er
eb
y
p
r
o
v
id
i
n
g
m
o
r
e
ac
cu
r
ac
y
.
Fig
u
r
e
8
also
p
r
ese
n
ts
th
e
c
o
m
p
ar
is
o
n
o
f
t
h
e
t
w
o
h
y
b
r
id
P
SO
-
A
N
FIS
a
n
d
W
T
-
A
N
FIS
m
o
d
els
b
et
w
ee
n
th
e
tar
g
e
t
an
d
th
e
f
o
r
ec
asted
o
u
tp
u
t.
T
h
e
t
w
o
h
y
b
r
id
m
o
d
els
i
n
d
icate
a
v
er
y
g
o
o
d
ac
cu
r
ac
y
f
o
r
th
e
f
o
r
ec
asti
n
g
b
y
p
r
o
v
id
in
g
a
g
o
o
d
co
r
r
elatio
n
b
etw
ee
n
t
h
e
m
e
asu
r
ed
o
u
tp
u
t a
n
d
t
h
e
f
o
r
ec
ast
ed
o
u
tp
u
t.
A
lt
h
o
u
g
h
th
e
t
w
o
m
o
d
els
p
r
o
v
e
to
b
e
g
o
o
d
m
et
h
o
d
s
f
o
r
s
o
lar
r
ad
iati
o
n
p
r
ed
ictio
n
,
th
e
W
T
-
A
NFI
S
m
o
d
el
p
r
o
v
id
es
a
b
etter
co
r
r
elatio
n
b
et
w
ee
n
t
h
e
m
ea
s
u
r
ed
an
d
f
o
r
ec
asted
o
u
tp
u
t,
h
en
ce
,
o
u
tp
er
f
o
r
m
i
n
g
t
h
e
P
SO
-
ANFI
S
m
o
d
el.
Fu
r
t
h
er
v
alid
atio
n
w
as
d
o
n
e
b
y
u
s
i
n
g
t
w
o
s
tatis
tical
e
v
alu
a
t
o
r
s
R
MSE
an
d
T
ab
le
1
p
r
ese
n
ts
t
h
e
s
tatis
t
ical
v
alu
e
s
o
f
t
h
e
t
w
o
h
y
b
r
id
m
o
d
els
o
b
tain
ed
at
b
o
th
th
e
tr
ai
n
in
g
a
n
d
test
i
n
g
s
ta
g
es.
Fro
m
T
a
b
le
1
,
w
h
en
t
h
e
o
b
tain
ed
R
MSE
an
d
R
²
v
al
u
e
s
f
r
o
m
t
h
e
t
w
o
h
y
b
r
id
m
o
d
el
s
w
er
e
co
m
p
ar
ed
,
th
e
W
T
-
ANFI
S
m
o
d
el
v
al
u
es
o
u
tp
er
f
o
r
m
s
t
h
at
o
f
t
h
e
P
SO
-
A
N
FIS
m
o
d
el
at
b
o
th
t
h
e
tr
ain
in
g
a
n
d
test
i
n
g
p
h
ase
s
.
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:
2
0
8
8
-
8708
S
o
la
r
r
a
d
ia
tio
n
f
o
r
ec
a
s
tin
g
in
N
ig
eria
b
a
s
ed
o
n
h
y
b
r
id
P
S
O
-
A
N
F
I
S
a
n
d
W
T
-
A
N
F
I
S
a
p
p
r
o
a
ch
(
S
a
n
i S
a
li
s
u
)
3923
(
a)
Tar
g
e
t
(
b
)
(
c)
(
d
)
Fig
u
r
e
7
.
Scatter
p
lo
ts
o
f
th
e
m
o
d
el
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h
er
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th
e
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y
b
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is
a
b
etter
m
o
d
el
f
o
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s
o
lar
r
ad
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n
p
r
ed
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n
f
o
r
th
e
s
elec
ted
lo
ca
tio
n
in
Ni
g
er
ia.
RE
F
E
R
E
NC
E
S
[1
]
S
.
Ka
rth
ik
e
y
a
n
,
e
t
a
l.
,
“
P
a
ra
m
e
t
ric
stu
d
ies
o
n
p
a
c
k
e
d
b
e
d
sto
ra
g
e
u
n
it
f
il
led
w
it
h
P
CM
e
n
c
a
p
s
u
late
d
sp
h
e
rica
l
c
o
n
tain
e
rs
f
o
r
lo
w
tem
p
e
ra
tu
re
so
lar
a
ir
h
e
a
ti
n
g
a
p
p
li
c
a
ti
o
n
s,
”
En
e
rg
y
Co
n
v
e
rs
io
n
a
n
d
M
a
n
a
g
e
me
n
t,
v
o
l.
7
8
,
p
p
.
7
4
-
8
0
,
2
0
1
4
.
[2
]
K.
Be
n
m
o
u
iza
a
n
d
A
.
Ch
e
k
n
a
n
e
,
“
F
o
re
c
a
stin
g
h
o
u
rly
g
lo
b
a
l
so
la
r
ra
d
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n
u
sin
g
h
y
b
rid
k
-
m
e
a
n
s
a
n
d
n
o
n
li
n
e
a
r
a
u
to
re
g
re
ss
iv
e
n
e
u
ra
l
n
e
tw
o
rk
m
o
d
e
ls,
”
En
e
rg
y
C
o
n
v
e
rs
io
n
a
n
d
M
a
n
a
g
e
me
n
t
,
v
o
l.
7
5
,
p
p
.
5
6
1
-
5
6
9
,
2
0
1
3
.
[3
]
J.
L
iu
,
e
t
a
l.
,
“
Ob
se
rv
a
ti
o
n
a
n
d
c
a
lcu
latio
n
o
f
th
e
so
lar
ra
d
iatio
n
o
n
t
h
e
T
ib
e
tan
P
late
a
u
,
”
En
e
rg
y
Co
n
v
e
rs
io
n
a
n
d
M
a
n
a
g
e
me
n
t,
v
o
l.
5
7
,
p
p
.
2
3
-
3
2
,
2
0
1
2
.
[4
]
A
.
M
a
g
h
ra
b
i,
“
P
a
ra
m
e
teriz
a
ti
o
n
o
f
a
si
m
p
le
m
o
d
e
l
to
e
sti
m
a
te
m
o
n
th
ly
g
lo
b
a
l
so
lar
ra
d
iat
io
n
b
a
se
d
o
n
m
e
teo
ro
lo
g
ica
l
v
a
riab
les
,
a
n
d
e
v
a
lu
a
ti
o
n
o
f
e
x
isti
n
g
so
lar
r
a
d
iatio
n
m
o
d
e
ls
f
o
r
T
a
b
o
u
k
,
S
a
u
d
i
A
ra
b
ia,
”
En
e
rg
y
c
o
n
v
e
rs
io
n
a
n
d
ma
n
a
g
e
m
e
n
t,
v
o
l.
5
0
,
p
p
.
2
7
5
4
-
2
7
6
0
,
2
0
0
9
.
[5
]
H.
Ga
rg
a
n
d
S
.
Ga
rg
,
“
P
re
d
icti
o
n
o
f
g
lo
b
a
l
so
lar
ra
d
iatio
n
f
ro
m
b
rig
h
t
su
n
s
h
in
e
h
o
u
rs
a
n
d
o
t
h
e
r
m
e
teo
ro
lo
g
ica
l
d
a
ta,
”
En
e
rg
y
Co
n
v
e
rs
io
n
a
n
d
M
a
n
a
g
e
me
n
t,
v
o
l.
2
3
,
p
p
.
1
1
3
-
1
1
8
,
1
9
8
3
.
[6
]
O.
A
ja
y
i,
e
t
a
l.
,
“
Ne
w
m
o
d
e
l
to
e
stim
a
te
d
a
il
y
g
lo
b
a
l
so
lar
ra
d
iatio
n
o
v
e
r
Nig
e
ria,
”
S
u
sta
in
a
b
le
E
n
e
rg
y
T
e
c
h
n
o
l
o
g
ies
a
n
d
Asse
ss
me
n
ts,
v
o
l.
5
,
p
p
.
2
8
-
36
,
2
0
1
4
.
[7
]
F
.
Be
sh
a
ra
t,
e
t
a
l.
,
“
E
m
p
iri
c
a
l
m
o
d
e
ls
f
o
r
e
sti
m
a
ti
n
g
g
lo
b
a
l
so
lar
ra
d
i
a
ti
o
n
:
A
re
v
ie
w
a
n
d
c
a
se
stu
d
y
,
”
Ren
e
wa
b
le
a
n
d
S
u
sta
i
n
a
b
le E
n
e
rg
y
Rev
iews
,
v
o
l.
2
1
,
p
p
.
7
9
8
-
8
2
1
,
2
0
1
3
.
[8
]
E.
F
a
lay
i,
e
t
a
l.
,
“
Em
p
iri
c
a
l
m
o
d
e
ls
f
o
r
th
e
c
o
rre
latio
n
o
f
g
lo
b
a
l
so
l
a
r
r
a
d
iatio
n
w
it
h
m
e
teo
ro
lo
g
ica
l
d
a
ta
f
o
r
Ise
y
in
,
Nig
e
ria,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Ph
y
sic
a
l
S
c
ien
c
e
s,
v
o
l.
3
,
p
p
.
2
1
0
-
2
1
6
,
2
0
0
8
.
[9
]
R.
L
.
F
a
g
b
e
n
le,
“
T
o
tal
so
lar
ra
d
iatio
n
e
stim
a
tes
in
Nig
e
ria
u
sin
g
a
m
a
x
i
m
u
m
-
li
k
e
li
h
o
o
d
q
u
a
d
ra
ti
c
f
it
,
”
Ren
e
wa
b
le
En
e
rg
y
,
v
o
l.
3
,
p
p
.
8
1
3
-
8
1
7
,
1
9
9
3
.
[1
0
]
A
.
S
a
m
b
o
,
“
E
m
p
iri
c
a
l
m
o
d
e
ls
f
o
r
th
e
c
o
rre
latio
n
o
f
g
lo
b
a
l
so
lar
ra
d
iatio
n
w
it
h
m
e
teo
ro
lo
g
ica
l
d
a
ta
f
o
r
n
o
rth
e
rn
Nig
e
ria,
”
S
o
la
r &
win
d
tec
h
n
o
lo
g
y
,
v
o
l.
3
,
p
p
.
8
9
-
9
3
,
1
9
8
6
.
[1
1
]
K.
Ch
it
e
k
a
a
n
d
C.
En
w
e
re
m
a
d
u
,
“
P
re
d
ict
io
n
o
f
g
lo
b
a
l
h
o
rizo
n
ta
l
so
lar
irrad
ian
c
e
in
Zi
m
b
a
b
w
e
u
s
in
g
a
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
s,
”
J
o
u
rn
a
l
o
f
Clea
n
e
r P
ro
d
u
c
ti
o
n
,
v
o
l
.
1
3
5
,
p
p
.
7
0
1
-
7
1
1
,
2
0
1
6
.
[1
2
]
S
.
Hu
ss
a
in
a
n
d
A
.
A
l
A
li
li
,
“
S
o
f
t
c
o
m
p
u
ti
n
g
a
p
p
r
o
a
c
h
f
o
r
so
lar
ra
d
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n
p
re
d
icti
o
n
o
v
e
r
A
b
u
Dh
a
b
i,
UA
E:
A
c
o
m
p
a
ra
ti
v
e
a
n
a
l
y
sis,
”
S
ma
r
t
En
e
rg
y
Gr
id
En
g
i
n
e
e
rin
g
(
S
EGE)
,
IE
EE
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
,
p
p
.
1
-
6
,
2
0
1
5
.
[1
3
]
G
.
Lan
d
e
ra
s,
e
t
a
l.
,
“
Co
m
p
a
riso
n
o
f
G
e
n
e
Ex
p
re
ss
io
n
P
r
o
g
ra
m
m
in
g
w
it
h
n
e
u
ro
-
f
u
z
z
y
a
n
d
n
e
u
ra
l
n
e
tw
o
rk
c
o
m
p
u
ti
n
g
tec
h
n
i
q
u
e
s
in
e
stim
a
ti
n
g
d
a
il
y
in
c
o
m
in
g
so
lar
ra
d
iatio
n
i
n
t
h
e
B
a
sq
u
e
Co
u
n
try
(No
rth
e
rn
S
p
a
in
),
”
En
e
rg
y
Co
n
v
e
rs
io
n
a
n
d
M
a
n
a
g
e
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6
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.
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[1
7
]
S
.
S
h
a
m
sh
irb
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n
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,
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t
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l.
,
“
Esti
m
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[1
8
]
J.
W
u
,
e
t
a
l.
,
“
P
re
d
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o
n
o
f
so
lar
ra
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w
it
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.
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3
9
,
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0
1
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.
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.
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o
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o
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t
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l.
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“
A
n
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w
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.
1
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1
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X
.
L
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a
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d
A
.
P
.
En
g
e
lb
re
c
h
t,
“
P
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3
3
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4
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0
0
7
.
[2
2
]
J.
S
.
Ja
n
g
,
“
A
NFIS
:
a
d
a
p
ti
v
e
-
n
e
tw
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rk
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b
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se
d
f
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tra
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3
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p
.
6
6
5
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6
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5
,
1
9
9
3
.
[2
3
]
M
.
M
u
sta
p
h
a
,
e
t
a
l.
,
“
Co
rre
lati
o
n
a
n
d
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v
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d
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y
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9
,
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0
1
6
.
[2
4
]
F
.
Ko
c
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ş
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n
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Ş
.
Ülk
e
r,
“
Esti
m
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tak
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u
ro
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p
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h
,
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v
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6
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p
.
4
8
9
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0
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2
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0
6
.
[2
5
]
J.
S
.
R.
Ja
n
g
,
e
t
a
l.
,
“
Ne
u
ro
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f
u
z
z
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n
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t
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p
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n
g
;
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p
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l
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telli
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,
”
1
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9
7
.
[2
6
]
T
.
N
g
u
y
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n
a
n
d
Y.
L
iao
,
“
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T
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m
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stin
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fe
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y
ste
m
,
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.
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7
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1
,
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0
1
1
.
[2
7
]
M
.
S
u
g
e
n
o
a
n
d
G
.
Ka
n
g
,
“
S
tru
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tu
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e
n
ti
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o
f
f
u
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z
y
m
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l,
”
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zz
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n
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ms
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v
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l.
2
8
,
p
p
.
1
5
-
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3
,
1
9
8
8
.
[2
8
]
R.
Eb
e
rh
a
rt
a
n
d
J.
Ke
n
n
e
d
y
,
“
A
n
e
w
o
p
ti
m
iz
e
r
u
sin
g
p
a
rti
c
le
sw
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r
m
th
e
o
r
y
,
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in
M
icr
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n
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u
ma
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ien
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e
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.
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HS
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5
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o
n
,
p
p
.
3
9
-
43
,
1
9
9
5
.
[2
9
]
S
.
Ra
j,
e
t
a
l.
,
“
C
a
rd
iac
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rrh
y
th
m
i
a
b
e
a
t
c
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ss
i
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ica
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sin
g
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n
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d
S
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M
,
”
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te
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me
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o
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.
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p
.
1
6
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-
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7
,
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0
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6
.
[3
0
]
H.
M
.
I.
P
o
u
sin
h
o
,
e
t
a
l.
,
“
S
h
o
rt
-
term
e
lec
tri
c
it
y
p
rice
s
f
o
re
c
a
stin
g
in
a
c
o
m
p
e
ti
ti
v
e
m
a
r
k
e
t
b
y
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h
y
b
rid
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–
A
NFIS
a
p
p
r
o
a
c
h
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
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o
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r
&
En
e
rg
y
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y
ste
ms
,
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o
l.
3
9
,
p
p
.
2
9
-
3
5
,
2
0
1
2
.
[3
1
]
W
.
Yu
a
n
d
X
.
L
i,
“
F
u
z
z
y
i
d
e
n
ti
f
ica
ti
o
n
u
si
n
g
f
u
z
z
y
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u
ra
l
n
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tw
o
rk
s
w
it
h
sta
b
le
lea
rn
i
n
g
a
lg
o
rit
h
m
s,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Fu
zz
y
S
y
st
e
ms
,
v
o
l.
1
2
,
p
p
.
4
1
1
-
4
2
0
,
2
0
0
4
.
[3
2
]
J.
Ra
f
iee
,
e
t
a
l.
,
“
A
n
o
v
e
l
tec
h
n
iq
u
e
f
o
r
se
lec
ti
n
g
m
o
th
e
r
w
a
v
e
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e
t
f
u
n
c
ti
o
n
u
sin
g
a
n
in
tel
l
i
g
e
n
t
f
a
u
lt
d
iag
n
o
sis
s
y
ste
m
,
”
Exp
e
rt S
y
ste
ms
wit
h
Ap
p
l
ica
ti
o
n
s,
v
o
l
.
3
6
,
p
p
.
4
8
6
2
-
4
8
7
5
,
2
0
0
9
.
[3
3
]
A
.
P
re
tt
o
,
e
t
a
l.
,
“
I
m
a
g
e
si
m
il
a
rit
y
b
a
se
d
o
n
Disc
re
te
W
a
v
e
let
T
ra
n
s
f
o
r
m
f
o
r
ro
b
o
ts
w
i
th
lo
w
-
c
o
m
p
u
tatio
n
a
l
re
so
u
rc
e
s,
”
Ro
b
o
ti
c
s a
n
d
Au
t
o
n
o
mo
u
s S
y
ste
ms
,
v
o
l.
5
8
,
p
p
.
8
7
9
-
8
8
8
,
2
0
1
0
.
[3
4
]
Z.
Ba
sh
ir
a
n
d
M
.
El
-
Ha
w
a
r
y
,
“
A
p
p
ly
in
g
w
a
v
e
lets
to
sh
o
rt
-
t
e
r
m
lo
a
d
f
o
re
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a
stin
g
u
sin
g
P
S
O
-
b
a
se
d
n
e
u
ra
l
n
e
tw
o
rk
s,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Po
we
r S
y
ste
ms
,
v
o
l.
2
4
,
p
p
.
2
0
-
2
7
,
2
0
0
9
.
B
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RAP
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AUTH
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B.
E
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.
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f
ro
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A
h
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a
d
u
Be
ll
o
Un
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y
,
Zaria
,
Ka
d
u
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S
tate
,
Nig
e
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S
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g
st
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Un
iv
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rsity
L
o
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d
o
n
,
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i
n
2
0
1
0
a
n
d
2
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1
3
re
sp
e
c
ti
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ly
.
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e
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c
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tl
y
a
P
h
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st
u
d
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n
t
a
t
th
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c
h
o
o
l
o
f
El
e
c
tri
c
a
l
E
n
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
la
y
sia
(
UT
M
).
He
is
a
lso
c
u
rre
n
tl
y
a
L
e
c
tu
re
r
a
t
th
e
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p
a
rtme
n
t
o
f
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e
c
tri
c
a
l
En
g
in
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e
rin
g
,
A
h
m
a
d
u
Be
ll
o
Un
iv
e
rsit
y
,
Zaria
,
Ka
d
u
n
a
S
tate
,
Nig
e
ria.
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is
a
re
g
istere
d
En
g
in
e
e
r
w
it
h
Co
u
n
c
il
f
o
r
t
h
e
re
g
u
latio
n
o
f
e
n
g
i
n
e
e
rin
g
in
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e
ria
(COREN),
M
e
m
b
e
r
o
f
th
e
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e
rian
S
o
c
iet
y
o
f
En
g
in
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e
rs
(M
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)
a
n
d
IEE
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stu
d
e
n
t
m
e
m
b
e
r.
His
re
se
a
rc
h
a
re
a
is
o
n
Re
n
e
w
a
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le
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n
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rg
y
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n
d
d
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b
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te
d
sy
ste
m
,
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e
rg
y
m
a
n
a
g
e
m
e
n
t
a
n
d
c
o
n
tro
l
,
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o
a
d
f
o
re
c
a
stin
g
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n
d
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n
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g
rid
/M
icr
o
g
rid
d
e
sig
n
a
n
d
p
lan
n
in
g
.
M
o
h
d
W
a
z
i
r
M
u
sta
fa
re
c
e
iv
e
d
h
is
B.
E
n
g
.
De
g
re
e
(1
9
8
8
),
M
.
S
c
.
(1
9
9
3
)
a
n
d
P
h
D
(
1
9
9
7
)
f
ro
m
Un
iv
e
rsit
y
o
f
S
trath
c
l
y
d
e
,
S
c
o
tl
a
n
d
,
UK
.
He
is
c
u
rre
n
tl
y
a
P
ro
f
e
ss
o
r
a
n
d
th
e
Ch
a
ir
o
f
th
e
S
c
h
o
o
l
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
la
y
sia
.
He
i
s
a
m
e
m
b
e
r
o
f
In
stit
u
ti
o
n
o
f
En
g
in
e
e
rs,
M
a
la
y
sia
(IE
M
)
a
n
d
a
m
e
m
b
e
r
o
f
IEE
E.
His
re
se
a
r
c
h
in
tere
st
in
c
lu
d
e
s
p
o
w
e
r
s
y
ste
m
sta
b
il
it
y
,
F
A
C
T
S
,
w
irel
e
ss
p
o
w
e
r
tran
sm
issio
n
a
n
d
p
o
w
e
r
s
y
ste
m
d
i
strib
u
ti
o
n
a
u
t
o
m
a
ti
o
n
.
M
a
m
u
n
u
M
u
sta
p
h
a
re
c
e
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v
e
d
h
is
B.
E
n
g
a
n
d
M
S
c
.
d
e
g
re
e
s
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Ba
y
e
ro
Un
iv
e
rsit
y
Ka
n
o
,
Ka
n
o
S
tate
,
Nig
e
ria,
a
n
d
M
S
c
in
El
e
c
tr
ica
l
En
g
in
e
e
rin
g
in
th
e
y
e
a
rs
2
0
0
3
a
n
d
2
0
1
1
re
sp
e
c
ti
v
e
l
y
.
H
e
re
c
e
i
v
e
d
h
is
P
h
D
in
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
lay
sia
in
th
e
y
e
a
r
2
0
1
8
.
He
is
c
u
rre
n
tl
y
a
S
e
n
io
r
Lec
tu
re
r
a
t
Ka
n
o
S
tate
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
Wu
d
il
,
Ka
n
o
S
tate
,
Nig
e
ria.
His
re
se
a
rc
h
a
r
e
a
in
c
lu
d
e
s
L
o
a
d
f
o
re
c
a
stin
g
,
Re
n
e
w
a
b
le
e
n
e
r
g
y
d
istri
b
u
te
d
sy
ste
m
s,
En
e
rg
y
m
a
n
a
g
e
m
e
n
t
a
n
d
c
o
n
tr
o
l.
He
is
a
re
g
istere
d
En
g
in
e
e
r
w
it
h
Co
u
n
c
il
f
o
r
th
e
re
g
u
latio
n
o
f
e
n
g
in
e
e
rin
g
i
n
Nig
e
ria (COREN),
M
e
m
b
e
r
o
f
th
e
Nig
e
rian
S
o
c
iety
o
f
En
g
in
e
e
rs (M
NSE
)
a
n
d
IEE
E
st
u
d
e
n
t
m
e
m
b
e
r.
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