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8708
,
DOI
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.
1
1
5
9
1
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j
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.
v7
i
4
.
pp
2
2
4
1
-
2252
2241
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cr
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[
1
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.
Hu
m
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ac
tiv
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s
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tr
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s
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u
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en
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o
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ate
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f
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ts
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lt
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e
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[
2
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.
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lect
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ic
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b
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v
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s
[
3
]
.
T
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in
cr
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s
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in
d
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d
f
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tr
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s
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m
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tio
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[
4
]
.
I
f
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s
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ch
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ac
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[
5
]
.
A
cc
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s
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
4
,
A
u
g
u
s
t
2
0
1
7
:
2
2
4
1
–
2
2
5
2
2242
S
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[
6
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.
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e
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
e
lo
ad
a
n
d
its
ex
o
g
e
n
o
u
s
f
ac
to
r
s
is
co
m
p
le
x
an
d
in
c
lu
d
es
n
o
n
-
l
in
ea
r
ch
ar
ac
ter
i
s
t
ics,
m
a
k
in
g
it
p
u
r
el
y
cr
u
ci
al
to
m
o
d
el
o
v
er
co
n
v
e
n
tio
n
al
tec
h
n
iq
u
e
s
[
7
]
.
T
h
e
last
f
e
w
y
ea
r
s
h
av
e
b
ee
n
a
lo
t
o
f
r
esear
c
h
t
h
at
d
is
c
u
s
s
es
t
h
e
d
e
v
elo
p
m
e
n
t
o
f
a
m
o
d
el
f
o
r
p
r
ed
ictio
n
o
f
ele
ctr
icit
y
co
n
s
u
m
p
tio
n
[
8
]
,
[
9
]
,
[
10
].
Ma
n
y
a
lg
o
r
ith
m
s
ar
e
ap
p
lied
f
o
r
f
o
r
ec
asti
n
g
w
i
th
ti
m
e
s
er
ies
m
o
d
el.
O
n
e
o
f
t
h
e
m
is
AR
I
M
A
m
o
d
eli
n
g
w
h
ich
h
as
an
ed
g
e
i
n
p
r
ed
ictin
g
th
e
ac
c
u
r
ac
y
o
f
t
h
e
d
ata
i
n
th
e
f
o
r
m
o
f
ti
m
e
s
er
ies
f
o
r
s
h
o
r
t
-
ter
m
p
er
io
d
s
.
On
e
r
eq
u
ir
e
m
en
t
is
t
h
e
r
e
g
r
ess
io
n
as
s
u
m
p
tio
n
s
m
u
s
t
b
e
f
u
lf
i
lled
,
w
h
er
e
d
ata
m
u
s
t
m
e
et
th
e
m
u
l
tiv
ar
iate
n
o
r
m
al
d
is
t
r
ib
u
tio
n
,
an
d
p
r
o
d
u
ce
th
e
s
a
m
e
co
v
ar
ian
ce
m
a
tr
ix
f
o
r
ea
ch
p
o
p
u
latio
n
[
11
]
.
T
o
i
m
p
r
o
v
e
th
e
d
r
a
w
b
ac
k
s
r
eg
r
ess
io
n
m
o
d
el,
A
r
t
if
icial
I
n
tel
lig
e
n
ce
o
n
e
t
h
at
i
s
Fu
z
z
y
S
u
g
e
n
o
ca
n
b
e
u
s
ed
.
T
h
is
m
et
h
o
d
h
as
f
r
eq
u
e
n
tl
y
b
ee
n
u
s
ed
to
ass
i
s
t
i
n
t
h
e
co
m
p
let
io
n
o
f
v
ar
io
u
s
k
i
n
d
s
o
f
p
r
ed
ictio
n
s
.
On
AR
I
M
A
,
t
h
e
m
ea
s
u
r
e
m
e
n
t
er
r
o
r
is
o
b
tain
ed
f
r
o
m
t
h
e
d
i
f
f
er
e
n
ce
b
et
wee
n
th
e
ac
tu
al
v
al
u
e
an
d
th
e
est
i
m
a
ted
v
alu
e,
w
h
ile
th
e
f
u
zz
y
lo
o
k
o
f
er
r
o
r
as a
h
az
in
es
s
in
t
h
e
m
o
d
el
p
ar
a
m
eter
s
.
Da
m
o
u
s
is
[
12
]
,
d
ev
elo
p
ed
a
f
u
zz
y
m
o
d
el
i
s
u
s
ed
to
d
et
er
m
in
e
t
h
e
w
i
n
d
s
p
ee
d
p
r
ed
ictio
n
s
th
a
t
g
en
er
ate
s
a
v
er
y
s
tr
o
n
g
co
r
r
elatio
n
w
it
h
th
e
v
al
u
e
o
f
Sp
ea
r
m
an
.
F
u
zz
y
S
u
g
en
o
ca
p
ab
le
o
f
r
eso
lv
in
g
th
e
p
r
o
b
lem
s
t
h
at
h
a
v
e
d
ata
in
th
e
f
o
r
m
o
f
ti
m
e
s
er
ies
w
it
h
a
f
air
l
y
h
i
g
h
d
eg
r
ee
o
f
ac
cu
r
ac
y
[
1
3
]
,
[
1
4
]
.
B
u
t
th
is
m
et
h
o
d
also
h
a
s
d
r
a
w
b
ac
k
s
in
th
e
d
eter
m
in
a
tio
n
r
u
le
b
ased
o
n
r
eg
r
es
s
io
n
f
u
n
ct
io
n
.
T
h
er
ef
o
r
e,
if
th
e
d
ata
th
a
t
is
u
s
ed
q
u
ite
a
lo
t
o
f
i
m
p
ac
t
o
n
th
e
i
n
cr
ea
s
i
n
g
l
y
co
m
p
le
x
r
u
le.
I
f
t
h
e
r
u
le
is
b
u
ilt
m
a
n
u
all
y
,
it
w
il
l
r
eq
u
ir
e
co
n
s
id
er
ab
le
ti
m
e
a
n
d
v
a
lu
e
s
f
o
r
th
e
co
ef
f
icie
n
ts
i
n
t
h
e
r
u
le
is
less
ac
cu
r
ate
b
ec
au
s
e
t
h
er
e
is
s
till
u
n
ce
r
tai
n
t
y
to
d
eter
m
i
n
atio
n
r
es
u
lti
n
g
er
r
o
r
lev
el
is
s
till
h
i
g
h
.
T
o
co
r
r
e
ct
d
ef
icien
cie
s
in
th
e
m
et
h
o
d
s
ab
o
v
e,
th
is
p
ap
er
atte
m
p
ts
co
ef
f
icie
n
t
o
p
ti
m
izati
o
n
o
n
S
u
g
en
o
f
u
zz
y
r
u
le
w
h
er
e
th
e
v
al
u
es
o
f
t
h
e
co
ef
f
ic
ien
t
r
u
le
au
to
m
atica
ll
y
g
en
er
ated
u
s
i
n
g
h
e
u
r
is
tic
al
g
o
r
ith
m
s
lik
e
E
v
o
l
u
tio
n
ar
y
Stra
teg
ie
s
.
T
h
is
e
f
f
o
r
t
w
i
ll
en
ab
le
to
r
aise
t
h
e
p
r
ed
ictio
n
ac
cu
r
ac
y
o
n
elec
tr
icit
y
co
n
s
u
m
p
tio
n
b
y
o
p
ti
m
i
zin
g
S
u
g
e
n
o
f
u
zz
y
m
eth
o
d
t
h
an
u
s
in
g
o
r
d
in
ar
y
r
eg
r
ess
io
n
.
I
n
its
ap
p
licatio
n
ev
o
lu
tio
n
al
g
o
r
ith
m
o
p
ti
m
iza
t
io
n
s
tr
ate
g
ies
p
r
o
v
e
n
to
f
i
n
is
h
o
n
f
u
zz
y
s
ets
a
n
d
ca
n
i
m
p
r
o
v
e
t
h
e
p
er
f
o
r
m
a
n
ce
b
etter
th
an
f
u
zz
y
m
eth
o
d
[
15
]
.
2.
RE
L
AT
E
D
WO
RK
Var
io
u
s
m
eth
o
d
s
u
s
ed
i
n
th
e
l
i
ter
atu
r
e
to
s
o
lv
e
t
h
e
p
r
o
b
lem
s
w
il
l
b
e
b
r
ief
l
y
d
is
c
u
s
s
ed
in
t
h
i
s
s
ec
tio
n
.
T
h
e
m
et
h
o
d
s
in
c
lu
d
e
T
i
m
e
s
er
ies an
al
y
s
i
s
-
b
ased
ap
p
r
o
ac
h
,
h
eu
r
is
tic
ap
p
r
o
ac
h
,
an
d
a
h
y
b
r
i
d
ap
p
r
o
ac
h
.
AR
I
M
A
is
a
m
et
h
o
d
o
f
ten
u
s
ed
f
o
r
f
o
r
ec
asti
n
g
.
T
s
en
g
[
16
]
,
E
d
ig
er
an
d
A
k
ar
[
17
]
,
He
et
al
[1
8
]
,
C
h
o
,
et
al
[
19
]
,
ap
p
ly
i
n
g
wh
ich
p
r
o
v
ed
A
R
I
M
A
t
i
m
e
s
er
ies
d
ata
th
at
ca
n
p
r
ed
ict
ac
cu
r
atel
y
w
h
er
e
th
e
m
ea
s
u
r
e
m
e
n
t
er
r
o
r
o
n
AR
I
M
A
o
b
tain
ed
f
r
o
m
th
e
d
i
f
f
er
en
c
e
b
et
w
ee
n
t
h
e
ac
t
u
al
d
ata
an
d
d
ata
esti
m
atio
n
,
s
o
th
at
t
h
i
s
m
o
d
el
r
eq
u
ir
es
a
lo
t
o
f
tr
ial
o
b
s
er
v
atio
n
i
n
f
o
r
ec
asti
n
g
.
I
n
f
ac
t,
t
h
e
r
ea
lit
y
o
f
d
ata
to
b
e
r
ar
el
y
p
r
ed
icted
r
ea
ch
es
th
e
n
u
m
b
er
o
f
o
b
s
er
v
atio
n
s
w
as
a
s
s
u
m
ed
as
p
r
o
n
e
to
f
l
u
ctu
a
tio
n
s
i
n
t
h
e
m
o
v
e
m
e
n
t
o
f
d
ata.
T
h
is
is
ca
u
s
i
n
g
th
e
er
r
o
r
v
alu
e
is
s
till
h
i
g
h
.
AR
I
M
A
ca
n
al
s
o
b
e
u
s
ed
in
ad
d
itio
n
to
o
th
e
r
m
et
h
o
d
s
s
u
c
h
as
A
r
ti
f
icial
Neu
r
al
Ne
t
w
o
r
k
(
ANN)
[
20
]
,
Gen
etic
A
l
g
o
r
ith
m
(
Gen
etic
Alg
o
r
it
h
m
)
a
n
d
th
e
m
o
s
t
p
o
p
u
lar
m
et
h
o
d
,
th
e
F
u
zz
y
L
o
g
ic
(
F
L
)
.
C
o
m
p
ar
ed
w
i
th
ANN,
F
u
zz
y
L
o
g
ic
o
f
f
er
s
a
c
lear
i
n
s
ig
h
t
i
n
t
o
th
e
m
o
d
el.
F
u
zz
y
f
o
r
ec
asti
n
g
s
y
s
te
m
ca
n
ca
p
tu
r
e
th
e
p
atter
n
o
f
p
ast d
ata
to
p
r
o
j
ec
t
d
ata
th
at
w
ill co
m
e.
E
lectr
ical
lo
ad
tim
e
s
er
ies
is
v
er
y
p
o
p
u
lar
an
d
F
u
zz
y
L
o
g
ic
i
n
d
ea
lin
g
w
it
h
th
e
s
y
s
te
m
s
h
a
p
ed
tim
e
s
s
er
ies
b
ec
a
u
s
e
it
i
s
co
n
s
id
er
ed
a
v
er
y
s
u
itab
le
f
u
zz
y
m
eth
o
d
f
o
r
f
o
r
ec
ast
in
g
e
lectr
icit
y
lo
ad
.
So
m
e
f
u
zz
y
lo
g
ic
h
as
f
r
eq
u
en
t
l
y
b
ee
n
u
s
ed
to
ass
is
t
in
t
h
e
co
m
p
letio
n
o
f
a
w
id
e
v
ar
iet
y
o
f
p
r
ed
ictio
n
s
s
u
ch
as
r
a
w
m
a
ter
ial
s
u
p
p
lier
s
p
r
ed
ictio
n
[
21
]
,
ex
ch
an
g
e
r
ate
[
22
]
,
o
r
elec
tr
ic
it
y
co
n
s
u
m
p
tio
n
f
o
r
ec
ast
[
23
]
.
Dam
o
u
s
i
s
[
12
]
,
i
m
p
le
m
en
t
s
f
u
zz
y
m
o
d
els
ar
e
u
s
ed
to
d
eter
m
in
in
g
th
e
w
i
n
d
s
p
ee
d
p
r
ed
ictio
n
s
an
d
p
r
o
d
u
c
e
th
e
f
i
n
al
r
es
u
lt
is
r
elev
an
t.
L
ie
et
a
l.
[
13
]
,
ca
lcu
latin
g
elec
tr
ical
e
n
er
g
y
n
ee
d
s
s
h
o
r
t
ter
m
b
y
u
s
i
n
g
t
h
e
ap
p
r
o
ac
h
o
f
g
r
e
y
-
b
a
s
ed
f
u
zz
y
th
a
t
ca
n
b
e
u
s
ed
as
an
o
p
er
atio
n
al
co
s
t
s
av
in
g
s
a
n
d
s
af
e
co
n
d
itio
n
s
th
at
allo
w
u
tili
tie
s
to
p
r
o
ce
s
s
p
r
o
d
u
ctio
n
r
eso
u
r
ce
s
to
o
p
ti
m
ize
en
er
g
y
p
r
ices
a
n
d
e
x
ch
a
n
g
e
w
i
th
p
r
o
d
u
ce
r
s
a
n
d
co
n
s
u
m
er
s
.
Allah
v
er
d
i,
et
al
.
[
24
]
,
T
alei
et
al
.
[
25
]
,
d
an
C
h
a
n
g
et
al
.
[
22
]
h
as
i
m
p
le
m
e
n
ted
a
f
o
r
ec
asti
n
g
an
d
Su
g
e
n
o
in
th
e
ca
s
e
o
f
d
ata
th
at
is
s
u
i
tab
le
f
o
r
ti
m
e
-
s
er
ies.
F
u
zz
y
S
u
g
e
n
o
also
h
as
a
w
ea
k
n
e
s
s
,
esp
ec
ial
l
y
i
n
t
h
e
r
u
le
s
ec
tio
n
T
HE
N,
n
a
m
el
y
t
h
e
e
x
i
s
ten
ce
o
f
m
at
h
e
m
a
tical
ca
lcu
latio
n
s
th
a
t
ca
n
n
o
t
p
r
o
v
id
e
a
n
atu
r
al
f
r
a
m
e
w
o
r
k
f
o
r
r
ep
r
esen
tin
g
h
u
m
an
k
n
o
w
led
g
e
i
n
tr
u
th
.
T
h
e
s
ec
o
n
d
p
r
o
b
lem
i
s
t
h
e
lac
k
o
f
f
r
ee
d
o
m
to
u
s
e
d
if
f
er
en
t
p
r
in
cip
le
s
i
n
f
u
zz
y
lo
g
ic
s
o
t
h
at
t
h
e
u
n
ce
r
tai
n
t
y
o
f
t
h
e
f
u
zz
y
s
y
s
te
m
ca
n
n
o
t b
e
r
ep
r
esen
ted
as
w
ell.
Heu
r
is
tic
al
g
o
r
ith
m
ca
n
h
e
lp
in
f
ix
in
g
d
r
a
w
b
ac
k
i
n
Fu
zz
y
S
u
g
e
n
o
.
Nik
d
el
et
al
[
26
]
,
Ma
r
iaj
ay
ap
r
ak
as
h
et
al
[
27
]
,
Nallasa
m
y
a
n
d
R
a
tn
a
v
el
u
h
a
s
i
m
p
le
m
en
ted
o
p
ti
m
izatio
n
o
n
Su
g
en
o
ca
n
p
r
o
v
id
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
R
u
le
Op
timiz
a
tio
n
o
f F
u
z
z
y
I
n
f
eren
ce
S
ystem
S
u
g
en
o
Usi
n
g
E
vo
lu
tio
n
S
tr
a
teg
y
fo
r
….
(
Ga
ya
tr
i D
w
i S
a
n
tika
)
2243
th
e
lev
el
o
f
ac
cu
r
ac
y
s
teep
er
,
an
d
o
p
tim
ized
f
u
zz
y
co
n
tr
o
ll
er
g
iv
es
b
etter
p
er
f
o
r
m
a
n
ce
th
an
a
co
n
v
en
tio
n
al
f
u
zz
y
co
n
tr
o
ller
a
ls
o
r
eg
a
r
d
i
n
g
r
i
s
in
g
a
n
d
s
ettli
n
g
ti
m
e.
E
v
o
lu
tio
n
Stra
te
g
ies
(
E
S)
i
s
an
ev
o
l
u
tio
n
ar
y
alg
o
r
ith
m
u
s
ed
in
th
i
s
s
t
u
d
y
a
s
an
o
p
ti
m
izatio
n
tech
n
iq
u
e.
S
u
ch
as
g
en
et
ic
alg
o
r
ith
m
s
,
th
i
s
m
e
th
o
d
h
as
a
h
i
g
h
s
u
cc
e
s
s
r
ate
in
s
o
l
v
i
n
g
o
p
ti
m
izatio
n
p
r
o
b
lem
s
in
co
m
p
u
ter
s
cien
ce
p
r
o
b
lem
s
.
E
S
h
a
s
b
ee
n
s
tr
o
n
g
er
an
d
as
o
p
tim
izatio
n
tec
h
n
iq
u
es to
t
h
e
p
r
o
b
lem
o
f
h
i
g
h
d
i
m
e
n
s
io
n
al
s
ea
r
ch
s
p
ac
e.
T
h
is
p
ap
er
atte
m
p
ts
to
f
ill
t
h
ese
k
n
o
w
led
g
e
g
ap
s
b
y
ad
d
r
ess
in
g
t
h
e
o
p
ti
m
izatio
n
r
u
le
o
n
f
u
zz
y
Su
g
en
o
u
s
i
n
g
h
e
u
r
is
tic
al
g
o
r
it
h
m
s
t
h
at
ar
e
ex
p
ec
ted
to
i
m
p
r
o
v
e
th
e
ac
cu
r
ac
y
o
f
f
o
r
ec
asti
n
g
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
3
.
1
.
F
uzzy
I
nfe
re
nce
Sy
s
t
e
m
S
ug
eno
T
h
is
s
t
u
d
y
u
s
es
a
m
o
d
el
f
u
z
z
y
i
n
f
er
en
ce
s
y
s
te
m
S
u
g
e
n
o
to
o
v
er
co
m
e
th
e
s
h
o
r
tco
m
i
n
g
s
t
h
at
ar
e
o
w
n
ed
b
y
s
ev
er
al
o
t
h
er
s
tu
d
ie
s
i
n
s
ec
t
io
n
2
f
o
r
f
o
r
ec
ast
in
g
p
r
o
b
lem
s
.
Fu
zz
y
Su
g
e
n
o
is
o
n
e
m
et
h
o
d
in
f
u
zz
y
lo
g
ic
i
n
tr
o
d
u
ce
d
b
y
T
ak
ag
i
-
S
u
g
e
n
o
Ka
n
g
[
28
]
.
Fu
zz
y
S
u
g
en
o
i
m
p
r
o
v
e
s
t
h
e
w
ea
k
n
e
s
s
es
p
o
s
s
ess
ed
b
y
p
u
r
e
f
u
zz
y
i
n
f
er
e
n
ce
s
y
s
te
m
to
ad
d
a
s
i
m
p
le
m
at
h
e
m
atica
l
ca
lc
u
latio
n
a
s
r
u
le
p
ar
t
T
HE
N.
On
th
is
ch
a
n
g
e,
th
e
f
u
zz
y
s
y
s
te
m
h
as a
w
ei
g
h
ted
av
er
ag
e
v
alu
e
i
n
t
h
e
r
u
le
s
s
ec
ti
o
n
f
u
zz
y
I
F
-
T
HE
N
[
29
]
.
On
e
-
Or
d
er
Fu
zz
y
Su
g
e
n
o
m
o
d
el
o
f
th
e
f
o
r
m
:
(
)
(
)
(2
)
W
h
er
e:
Xij
: v
alu
e
w
e
ig
h
t c
r
iter
ia
till
-
j
th
at
r
elev
an
t
w
o
r
k
b
y
r
u
les t
ill
-
i
A
1
: f
u
zz
y
s
et
f
o
r
v
ar
iab
le
w
ei
g
h
t
i
n
g
cr
iter
ia
f
o
r
all
r
elev
an
t r
u
le
s
till
-
j
w
h
o
r
ele
v
an
t
u
n
til r
u
le
s
till
-
i
º
:
o
p
er
ato
r
A
ND
n
: n
u
m
b
e
r
o
f
cr
iter
ia
A
i
: c
o
n
s
tan
ta
v
al
u
e
till
-
i
P1
: c
o
n
s
tan
t i
n
t
h
e
co
n
s
eq
u
en
t
q
: d
ec
is
io
n
1)
F
uzzif
ica
t
io
n
Fu
zz
i
f
icatio
n
i
s
a
p
r
o
ce
s
s
o
f
ch
a
n
g
i
n
g
t
h
e
cr
ip
s
v
al
u
e
i
n
to
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
s
[
21
]
.
I
n
th
e
f
u
zz
if
ica
tio
n
,
th
e
p
ar
a
m
e
ter
is
r
ep
r
esen
ted
as
a
v
ar
iab
le
in
p
u
t.
P
ar
a
m
eter
s
u
s
ed
as
in
p
u
t
v
ar
iab
les
ar
e
p
r
esen
ted
in
T
ab
le
I
.
T
h
e
o
u
tp
u
t
v
ar
iab
les
in
th
i
s
s
tu
d
y
o
f
t
h
e
r
es
u
lts
f
o
r
m
as
t
h
e
p
r
ed
icti
o
n
.
T
h
is
s
t
u
d
y
u
s
e
s
th
r
ee
in
p
u
t
v
ar
iab
les
ar
e
s
h
o
w
n
in
T
ab
le
1
.
A
ll
f
i
v
e
o
f
th
ese
v
ar
iab
le
s
h
a
v
e
th
e
f
u
z
z
y
s
ets
a
n
d
s
a
m
e
b
o
u
n
d
ar
ies
f
u
zz
y
m
e
m
b
er
s
h
i
p
.
T
h
e
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
in
t
h
e
i
n
p
u
t
v
ar
iab
les
i
s
r
ep
r
esen
ted
w
it
h
r
ea
l
n
u
m
b
er
s
.
Fig
u
r
e
1
t
h
er
e
i
s
a
d
o
m
a
in
w
h
er
e
d
iv
id
ed
in
to
t
h
r
ee
f
u
z
z
y
s
u
b
s
et
s
lab
eled
a
s
―
i
n
cr
ea
s
in
g
‖,
―stab
le‖
a
n
d
―
d
ec
r
ea
s
i
n
g
‖
w
h
er
e
f
u
zz
y
s
u
b
s
ets’
i
n
cr
ea
s
i
n
g
h
a
v
e
li
m
i
te
d
[
-
1500
-
2
1
0
0
]
,
f
u
zz
y
s
u
b
s
ets
’
co
n
s
ta
n
t
h
av
e
li
m
it[
-
1500
-
1
5
0
0
]
an
d
f
u
zz
y
s
u
b
s
et
s
'
d
ec
r
ea
s
in
g
h
a
v
e
li
m
it
ed
[
1
5
0
0
-
2
1
0
0
]
.
T
ab
le
1
.
P
ar
am
eter
d
escr
ip
tio
n
No
P
a
r
a
me
t
e
r
D
e
scri
p
t
i
o
n
1
Y(t
-
1)
a
n
h
o
u
r
b
e
f
o
r
e
2
Y(t
-
2)
a
d
a
y
b
e
f
o
r
e
3
Y(t
-
3)
a
w
e
e
k
b
e
f
o
r
e
Fig
u
r
e
1
.
Me
m
b
er
s
h
ip
f
u
n
ctio
n
o
n
F
u
zz
y
Su
g
e
n
o
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
4
,
A
u
g
u
s
t
2
0
1
7
:
2
2
4
1
–
2
2
5
2
2244
Fo
r
ea
ch
m
e
m
b
er
s
h
ip
f
u
n
c
tio
n
s
,
in
p
u
t
v
ar
iab
le
w
h
er
e
μ
is
t
h
e
d
eg
r
ee
o
f
m
e
m
b
er
s
h
ip
an
d
x
is
th
e
v
al
u
e
o
f
th
e
in
p
u
t to
b
e
co
n
v
er
ted
i
n
to
a
f
u
zz
y
s
et
d
escr
ib
ed
in
E
q
u
atio
n
3
an
d
E
q
u
atio
n
4.
{
0
(
3
)
{
0
(
)
(
4
)
{
0
(5
)
2)
Rule
B
a
s
ed
T
h
is
s
ec
tio
n
i
s
t
h
e
f
o
r
m
at
io
n
o
f
a
f
u
zz
y
k
n
o
w
led
g
e
b
ase
(
i
f
.
.
.
th
en
r
u
les).
C
alc
u
latio
n
o
f
t
h
e
n
u
m
b
er
o
f
r
u
les
is
b
y
m
u
ltip
l
y
i
n
g
t
h
e
n
u
m
b
er
o
f
f
u
zz
y
s
et
s
(
t
w
o
lin
g
u
i
s
tic
v
ar
iab
le
s
)
as
th
e
n
u
m
b
er
o
f
in
p
u
t
v
ar
iab
les.
T
h
e
estab
lis
h
m
en
t
o
f
r
u
les
o
n
r
esear
ch
u
s
in
g
an
y
co
m
b
i
n
atio
n
o
f
as
m
a
n
y
as
2
to
th
e
p
o
w
e
r
in
p
u
t
th
r
ee
w
h
ich
is
t
h
e
s
u
m
o
f
m
a
n
y
v
ar
iab
les
r
esu
lti
n
g
i
n
2
7
r
u
le
[
2
2
]
.
On
F
u
zz
y
S
u
g
e
n
o
m
o
d
el
h
as
o
u
tp
u
t
(
co
n
s
eq
u
e
n
t)
s
y
s
te
m
i
s
n
o
t
i
n
th
e
f
o
r
m
o
f
a
f
u
zz
y
s
et,
b
u
t
i
n
t
h
e
f
o
r
m
o
f
lin
ea
r
E
q
u
atio
n
s
as
in
E
q
u
ati
o
n
2
.
E
x
a
m
p
les
o
f
r
u
les t
h
e
r
u
le
o
f
e
x
p
er
ts
to
p
r
ed
ict
th
e
d
e
m
a
n
d
f
o
r
elec
tr
icit
y
in
T
ab
le
2
.
T
a
b
le 2
.
Ru
le E
x
a
m
p
le
I
F
Y
(
t
-
1
)
h
i
g
h
A
N
D
Y
(
t
-
2
)
h
i
g
h
A
N
D
Y
(
t
-
3
)
h
i
g
h
T
H
EN
a
+
b
1
*
Y
(
t
-
1
)
+
b
2
*
Y
(
t
-
2
)
+
b
3
*
Y
(
t
-
3
)
I
F
Y
(
t
-
1
)
h
i
g
h
A
N
D
Y
(
t
-
2
)
h
i
g
h
A
N
D
Y
(
t
-
3
)
l
o
w
TH
EN
c
+
d
1
*
Y
(
t
-
1
)
+
d
2
*
Y
(
t
-
2
)
+
d
3
*
Y
(
t
-
3
)
I
F
Y
(
t
-
1
)
h
i
g
h
A
N
D
Y
(
t
-
2
)
l
o
w
A
N
D
Y
(
t
-
3
)
h
i
g
h
T
H
EN
e
+
f
1
*
Y
(
t
-
1
)
+
f
2
*
Y
(
t
-
2
)
+
f
3
*
Y
(
t
-
3
)
I
F
Y
(
t
-
1
)
l
o
w
A
N
D
Y
(
t
-
2
)
h
i
g
h
A
N
D
Y
(
t
-
3
)
l
o
w
T
H
EN
g
+
h
1
*
Y
(
t
-
1
)
+
h
2
*
Y
(
t
-
2
)
+
h
3
*
Y
(
t
-
3
)
I
F
Y
(
t
-
1
)
l
o
w
A
N
D
Y
(
t
-
2
)
l
o
w
A
N
D
Y
(
t
-
3
)
l
o
w
T
H
EN
i
+
j
1
*
Y(t
-
1
)
+
j
2
*
Y
(
t
-
2
)
+
j
3
*
Y
(
t
-
3)
3)
Def
uzzif
ica
t
io
n
T
h
e
m
et
h
o
d
u
s
e
s
is
t
h
e
m
ea
n
(
a
v
er
ag
e)
[
32
]
.
Α
-
p
r
e
d
icate
f
u
n
ctio
n
d
eter
m
i
n
atio
n
an
d
t
h
e
d
eter
m
in
at
io
n
o
f
α
-
p
r
ed
icate
×
o
u
tp
u
t
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
(
Z
)
.
Ou
tp
u
t
i
n
f
er
en
ce
t
h
e
r
esu
lt
s
o
f
ea
c
h
r
u
le
is
g
iv
e
n
e
x
p
licitl
y
(
cr
ip
s
)
b
y
α
-
p
r
ed
icate
(
f
ir
e
s
tr
en
g
t
h
)
.
Hav
in
g
o
b
tain
ed
th
e
v
a
lu
e
o
f
α
i,
th
en
th
e
n
ex
t
w
ill
b
e
th
e
p
r
o
ce
s
s
o
f
ca
lc
u
lat
in
g
t
h
e
v
al
u
e
o
f
ea
c
h
co
n
s
eq
u
en
t
an
y
r
u
le
s
(
zi)
in
ac
co
r
d
an
ce
w
it
h
t
h
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
ar
e
u
s
ed
.
Def
u
zz
if
ic
atio
n
E
q
u
atio
n
m
o
d
el
as in
E
q
u
atio
n
6.
∑
∑
(
6
)
T
h
e
ab
o
v
e
E
q
u
atio
n
s
,
α
i is t
h
e
an
tece
d
en
t
m
e
m
b
er
s
h
ip
v
a
lu
e
,
an
d
zi
is
th
e
r
u
le
i
n
f
er
en
ce
s
y
s
te
m
r
es
u
lt.
Fu
zz
y
Su
g
e
n
o
s
u
i
ted
to
f
ix
th
e
ti
m
es
s
er
ie
s
p
r
o
b
lem
s
[
31
]
.
B
u
t,
f
o
r
o
b
tain
in
g
o
p
ti
m
al
s
y
s
te
m
p
er
f
o
r
m
a
n
ce
,
t
h
e
co
ef
f
icie
n
t
b
y
t
h
e
r
u
les
s
h
o
u
ld
b
e
d
eter
m
i
n
ed
w
ith
p
r
ec
is
io
n
.
E
v
o
l
u
t
io
n
Stra
te
g
ies
(
E
S)
alg
o
r
ith
m
s
s
u
ited
to
s
ee
k
co
ef
f
icie
n
t
ap
p
r
o
p
r
iate
o
n
th
e
b
asis
o
f
th
e
r
u
les
o
f
t
h
e
f
ac
t
th
at
in
d
eter
m
i
n
i
n
g
t
h
e
v
alu
e
i
s
s
till
n
ee
d
to
test
s
e
v
er
al
ti
m
es
b
ef
o
r
eh
a
n
d
s
o
e
x
p
ec
t
E
v
o
l
u
tio
n
Stra
te
g
ies
ab
le
to
o
b
tain
a
s
o
lu
tio
n
to
th
e
p
r
o
b
le
m
s
o
f
o
p
ti
m
al
o
r
n
ea
r
-
o
p
ti
m
al
s
o
as
to
b
e
o
p
ti
m
ized
co
ef
f
icie
n
t
v
al
u
es
in
t
h
e
r
u
le
b
ase
t
h
a
t
w
a
s
cr
ea
ted
ea
r
lier
.
A
s
to
ch
a
s
tic
co
m
p
o
n
e
n
t
is
o
n
e
o
f
t
h
e
m
o
s
t
s
u
cc
e
s
s
f
u
l
m
eth
o
d
s
f
o
r
th
e
g
lo
b
al
o
p
ti
m
izatio
n
p
r
o
b
lem
;
th
e
y
ac
ce
p
t
to
r
ef
u
s
e
f
r
o
m
lo
ca
l
o
p
ti
m
a
an
d
af
f
ec
ted
p
r
em
atu
r
e
s
ta
g
n
atio
n
.
A
f
a
m
o
u
s
clas
s
o
f
g
lo
b
al
o
p
tim
izatio
n
m
eth
o
d
s
i
s
s
u
cc
e
s
s
f
u
l
ev
o
lu
tio
n
ar
y
s
tr
at
eg
y
w
it
h
i
n
th
e
r
ea
l
-
v
al
u
ed
s
o
l
u
tio
n
s
.
Fo
r
b
lack
-
b
o
x
o
p
ti
m
izatio
n
,
i.e
.
,
f
o
r
o
p
ti
m
izatio
n
s
ce
n
ar
io
s
,
E
v
o
l
u
tio
n
s
tr
ate
g
ies
ad
d
th
e
m
o
s
t
p
o
w
er
f
u
l
ev
o
l
u
tio
n
ar
y
m
et
h
o
d
s
,
w
h
er
e
n
o
f
u
n
ctio
n
a
l
ex
p
r
ess
io
n
s
ar
e
clea
r
l
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g
i
v
e
n
,
an
d
n
o
d
er
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b
e
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m
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ted
.
I
n
th
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e
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f
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h
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e
v
o
lu
t
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tr
ate
g
ies
w
i
ll
p
la
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a
n
i
m
p
o
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tan
t
r
o
le.
T
h
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ar
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ad
ap
ted
to
th
e
b
io
lo
g
ical
p
r
in
cip
le
o
f
ev
o
lu
tio
n
.
≤
−
500
−
500
≤
≤
500
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0
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500
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I
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N:
2
0
8
8
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8708
R
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2245
P
ar
am
eter
m
u
tatio
n
r
ate
o
r
th
e
p
r
o
b
ab
ilit
y
o
f
m
u
tatio
n
(
p
m
)
is
u
s
ed
to
d
eter
m
i
n
e
th
e
n
u
m
b
er
o
f
ch
r
o
m
o
s
o
m
e
s
t
h
at
h
a
v
e
m
u
t
atio
n
s
i
n
th
e
p
o
p
u
latio
n
.
T
h
e
p
r
o
ce
s
s
ca
r
r
ied
o
u
t
b
y
t
h
e
m
eth
o
d
o
f
r
an
d
o
m
m
u
tatio
n
o
f
g
e
n
es.
T
h
e
n
e
w
c
h
r
o
m
o
s
o
m
e
g
e
n
er
ated
is
d
escr
ib
ed
in
E
q
u
atio
n
7
[
32
]
.
(
0
)
(
7)
T
h
e
f
o
r
m
u
la
u
s
ed
to
f
i
n
d
th
e
v
alu
e
o
f
N
(
0
.
1
)
d
escr
ib
ed
in
E
q
u
atio
n
8.
(
0
)
√
−
2
2
(
8)
Self
-
ad
ap
tatio
n
f
o
r
m
ed
f
r
o
m
th
e
m
ea
s
u
r
in
g
s
tep
σ
in
th
e
ch
r
o
m
o
s
o
m
es
t
h
at
h
a
v
e
a
ch
an
g
e
o
f
v
ar
iatio
n
a
n
d
s
elec
tio
n
t
h
at
i
s
a
m
u
ta
tio
n
t
h
at
is
r
ea
lized
b
y
r
ep
lacin
g
t
h
e
p
ar
en
t to
o
f
f
s
p
r
in
g
as i
n
E
q
u
atio
n
8
.
We
p
r
o
p
o
s
e
a
m
eth
o
d
E
S
(
μ
+
λ
)
th
at
d
o
n
o
t
u
s
e
r
ec
o
m
b
in
at
io
n
an
d
s
elec
t
io
n
p
r
o
ce
s
s
in
v
o
l
v
in
g
in
d
iv
id
u
als
an
d
t
h
e
p
ar
en
t
wh
er
e
o
n
e
p
ar
en
t
is
ta
k
e
n
f
r
o
m
t
h
e
r
eg
r
e
s
s
io
n
E
q
u
atio
n
.
E
S
(
μ
+
λ
)
d
o
n
o
t
u
s
e
r
ec
o
m
b
i
n
atio
n
an
d
s
elec
tio
n
p
r
o
ce
s
s
in
v
o
l
v
in
g
th
e
u
s
e
o
f
eli
tis
m
s
elec
tio
n
o
f
i
n
d
i
v
id
u
al
o
f
f
s
p
r
in
g
a
n
d
p
ar
en
t
.
So
m
e
n
o
tatio
n
u
s
ed
b
y
E
S
a
s
μ
(
m
u
)
d
eter
m
i
n
e
t
h
e
s
ize
o
f
t
h
e
p
o
p
u
latio
n
(
as
p
o
p
Size
o
n
G
As)
an
d
λ
(
la
m
b
d
a)
s
p
ec
if
ies
t
h
e
n
u
m
b
er
o
f
o
f
f
s
p
r
in
g
p
r
o
d
u
ce
d
in
th
e
r
ep
r
o
d
u
ctiv
e
p
r
o
ce
s
s
(
s
a
m
e
as
cr
o
s
s
o
v
er
r
ate
an
d
m
u
tatio
n
r
ate
in
G
A
s
)
.
B
ec
au
s
e
E
S
w
a
s
r
el
y
in
g
o
n
m
o
r
e
m
u
tatio
n
s
,
t
h
e
r
ec
o
m
b
i
n
atio
n
p
r
o
ce
s
s
is
n
o
t
al
w
a
y
s
u
s
ed
.
R
ec
o
m
b
in
at
io
n
a
n
d
m
u
tatio
n
i
s
a
g
e
n
etic
ca
r
r
ier
.
T
h
e
ef
f
ec
ti
v
en
e
s
s
o
f
r
ec
o
m
b
i
n
at
io
n
is
v
er
y
li
m
ited
w
h
e
n
m
o
s
t
o
f
th
e
p
o
p
u
lat
io
n
in
to
h
o
m
o
g
e
n
eo
u
s
[
33
]
.
T
h
er
ef
o
r
e,
th
e
m
u
ta
tio
n
b
ei
n
g
t
h
e
o
n
l
y
m
et
h
o
d
to
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
.
A
t
Fig
u
r
e
2
,
w
h
e
n
th
e
e
s
tab
li
s
h
m
e
n
t
o
f
t
h
e
r
u
le
i
n
t
h
e
S
u
g
en
o
f
u
zz
y
al
w
a
y
s
g
e
n
er
ate
a
r
eg
r
ess
io
n
m
o
d
el
w
h
er
e
―
a‖
―
b
"
co
ef
f
icie
n
t
v
a
lu
e
g
en
er
ated
i
n
ea
ch
r
u
l
e.
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o
p
r
o
d
u
ce
o
p
tim
u
m
ac
c
u
r
ac
y
ap
p
r
o
ac
h
in
g
p
-
v
alu
e
t
h
e
co
ef
f
icie
n
t
is
g
e
n
e
r
ated
au
to
m
atica
ll
y
b
y
u
s
in
g
E
S.
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h
e
p
r
o
ce
s
s
ca
r
r
ied
o
u
t
b
y
th
e
m
et
h
o
d
o
f
r
an
d
o
m
m
u
tatio
n
o
f
g
e
n
es.
At
th
i
s
m
u
tatio
n
p
r
o
ce
s
s
,
w
e
e
n
t
er
o
n
e
o
f
th
e
f
u
n
ctio
n
s
o
f
li
n
e
ar
r
eg
r
ess
io
n
w
er
e
u
s
ed
as o
n
e
o
f
t
h
e
i
n
d
iv
id
u
als
w
it
h
t
h
e
h
o
p
e
o
f
p
r
o
d
u
cin
g
a
n
ea
r
-
p
er
f
ec
t s
co
r
e.
T
h
e
ef
f
ec
ti
v
e
n
ess
o
f
r
ec
o
m
b
in
atio
n
is
v
er
y
li
m
ited
w
h
en
m
o
s
t
o
f
t
h
e
p
o
p
u
latio
n
i
n
to
a
co
m
p
lex
t
h
at
m
u
tatio
n
to
b
e
th
e
o
n
l
y
m
et
h
o
d
to
p
r
o
d
u
ce
o
f
f
s
p
r
in
g
.
P
ar
am
eter
m
u
tatio
n
r
ate
o
r
t
h
e
p
r
o
b
ab
ilit
y
o
f
m
u
tatio
n
(
p
m
)
i
s
u
s
ed
to
d
eter
m
in
e
th
e
n
u
m
b
er
o
f
c
h
r
o
m
o
s
o
m
e
s
t
h
at
h
av
e
m
u
tatio
n
s
i
n
t
h
e
p
o
p
u
latio
n
.
D
u
r
i
n
g
th
e
r
ep
r
o
d
u
ctiv
e
p
h
ase,
all
th
e
o
f
f
s
p
r
in
g
p
r
o
d
u
ce
d
b
y
th
e
m
u
tatio
n
s
s
to
r
ed
o
n
o
f
f
s
p
r
in
g
p
o
o
l.
T
h
e
s
elec
tio
n
m
et
h
o
d
is
u
s
ed
to
d
eter
m
in
e
t
h
e
c
h
r
o
m
o
s
o
m
es
o
f
th
e
cu
r
r
en
t
p
o
p
u
latio
n
an
d
o
f
f
s
p
r
in
g
w
h
o
w
i
ll
b
e
elec
ted
to
th
e
n
e
x
t
g
en
er
atio
n
.
Of
f
s
p
r
in
g
p
r
o
d
u
ce
d
b
y
th
e
m
u
tatio
n
s
w
ill
b
e
s
elec
ted
if
th
e
y
h
a
v
e
a
b
etter
f
itn
es
s
v
alu
e.
I
n
th
e
p
r
o
ce
s
s
d
ef
u
zz
i
f
icatio
n
it
w
il
l
p
r
o
d
u
ce
a
co
n
s
tan
t
t
h
at
w
il
l
b
e
u
s
ed
to
g
et
t
h
e
z
v
alu
e
to
b
e
co
m
p
ar
ed
w
it
h
th
e
ac
tu
al
d
ata
to
g
et
an
er
r
o
r
r
es
u
lti
n
g
i
n
a
v
al
u
e
o
f
f
i
tn
e
s
s
.
T
h
e
lar
g
er
th
e
f
itn
e
s
s
v
al
u
e,
t
h
e
b
etter
th
e
r
e
s
u
l
tin
g
p
r
ed
ictio
n
s
.
T
h
e
p
u
r
p
o
s
e
o
f
t
h
e
u
s
e
o
f
E
S
f
o
r
o
p
ti
m
izat
io
n
o
n
S
u
g
e
n
o
f
u
zz
y
to
o
b
tai
n
o
p
ti
m
al
s
o
lu
tio
n
s
b
y
o
p
tim
izin
g
t
h
e
p
ar
a
m
eter
s
o
f
th
e
co
n
s
eq
u
e
n
t
p
ar
t
o
f
t
h
e
f
u
zz
y
S
u
g
en
o
.
I
l
lu
s
tr
atio
n
b
eh
av
io
r
E
v
o
l
u
tio
n
Stra
teg
ie
s
is
t
h
e
in
telli
g
en
t a
l
g
o
r
ith
m
s
to
f
i
n
d
a
s
o
lu
tio
n
as i
n
Fig
u
r
e
3
.
Fig
u
r
e
2
.
P
h
ase
o
f
o
p
ti
m
izatio
n
f
u
zz
y
s
u
g
e
n
o
u
s
in
g
E
v
o
l
u
tio
n
Stra
te
g
ies
I
n
Fig
u
r
e
3
s
h
o
w
s
th
e
p
o
in
ts
i
s
r
ep
r
esen
tativ
e
o
f
t
h
e
p
r
o
b
le
m
s
to
b
e
s
o
lv
ed
.
I
n
th
e
f
ir
s
t
p
o
in
t
o
f
th
e
tr
o
u
b
le
s
p
o
ts
s
p
r
ea
d
ac
r
o
s
s
th
e
ar
ea
o
f
th
e
s
o
lu
tio
n
.
B
u
t
o
v
er
ti
m
e
w
ill
ac
cu
m
u
late
i
n
to
an
ar
ea
clo
s
er
to
th
e
s
o
lu
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
4
,
A
u
g
u
s
t
2
0
1
7
:
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2
4
1
–
2
2
5
2
2246
Fig
u
r
e
3
.
I
llu
s
tr
atio
n
o
f
E
v
o
l
u
t
io
n
Stra
teg
ies
4.
DATA CO
L
L
E
C
T
I
O
N
Data
f
r
o
m
I
E
SO
De
m
a
n
d
On
t
ar
io
(
h
ttp
://
www
.
ie
s
o
.
ca
/)
.
I
E
SO
is
a
n
o
f
f
icial
w
eb
s
i
te
O
n
t
ar
io
p
o
w
er
au
th
o
r
it
y
.
I
E
SO
atte
m
p
t
s
er
v
i
ce
s
,
k
n
o
w
led
g
e
an
d
r
ea
s
o
n
in
g
to
s
u
p
p
o
r
t
On
tar
io
'
s
v
ar
ied
elec
tr
icit
y
s
y
s
te
m
.
T
h
e
I
E
SO
r
eliab
l
y
o
p
er
ates
t
h
e
s
y
s
te
m
i
n
r
ea
l
ti
m
e.
T
h
e
d
at
a
u
s
ed
in
th
is
r
e
s
ea
r
ch
co
n
s
i
s
t
ed
o
f
5
8
0
9
th
e
d
ail
y
lo
ad
d
ata
in
a
p
er
io
d
o
f
o
n
e
y
e
ar
.
T
h
e
ex
a
m
p
le
s
o
f
t
h
e
d
a
ta
i
n
o
n
e
d
a
y
s
h
o
w
i
n
T
ab
le
3
.
W
e
o
b
tain
ed
t
h
e
d
ata
f
r
o
m
I
E
SO
p
r
o
ce
s
s
ed
in
to
t
h
e
r
eg
r
ess
io
n
m
o
d
el
u
s
i
n
g
t
h
r
ee
p
er
io
d
s
.
E
lectr
i
c
lo
a
d
m
o
d
el
h
as
a
v
o
lat
ile
p
atter
n
.
T
h
e
s
h
ap
ed
p
atter
n
o
f
elec
tr
icit
y
co
n
s
u
m
p
tio
n
cy
cl
e
o
n
h
i
s
to
r
ical
d
ata
p
r
ev
io
u
s
l
y
.
T
h
e
c
y
cle
i
s
f
o
r
m
ed
b
ased
o
n
th
e
d
ata
o
f
t
h
e
p
r
ev
io
u
s
h
o
u
r
(
y
(
t
-
1
)
)
,
o
n
e
d
ay
a
g
o
(
y
(
t
-
2
)
)
,
an
d
s
ev
e
n
d
a
y
s
a
g
o
(
y
(
t
-
3
)
)
.
T
ab
le
3
.
T
h
e
ex
a
m
p
le
o
f
g
e
n
er
ated
ti
m
e
s
er
ies
m
o
d
el
A
c
t
u
a
l
(
t
)
t
-
1
t
-
2
t
-
3
1
4
0
3
9
1
4
8
1
5
1
1
8
4
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3
5
9
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1
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1
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3
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9
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1
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3
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
R
u
le
Op
timiz
a
tio
n
o
f F
u
z
z
y
I
n
f
eren
ce
S
ystem
S
u
g
en
o
Usi
n
g
E
vo
lu
tio
n
S
tr
a
teg
y
fo
r
….
(
Ga
ya
tr
i D
w
i S
a
n
tika
)
2247
5.
NUM
E
RICAL
E
XAM
P
L
E
5
.
1
.
G
ener
a
t
io
n Co
ef
f
icient
Rule
F
uzzy
Su
g
eno
Gen
er
atio
n
I
F..
T
HE
N
r
u
le
i
n
o
n
e
-
o
r
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er
o
n
Fu
zz
y
Su
g
e
n
o
,
th
er
e
is
ti
m
e
s
er
ies
a
n
al
y
s
i
s
f
o
r
m
u
la
(
s
ee
Fo
r
m
u
la
2
)
.
I
n
t
h
i
s
s
ec
t
io
n
,
th
e
r
esear
ch
er
w
i
ll
g
en
er
ate
r
u
le
u
s
i
n
g
E
v
o
lu
tio
n
S
tr
ateg
i
es.
Fo
r
r
eg
r
ess
io
n
,
ca
lcu
latio
n
s
li
k
e
i
n
s
ec
tio
n
3
.
T
h
e
r
esu
lts
o
b
tai
n
ed
ea
ch
h
o
u
r
lo
ad
v
alu
e
o
n
co
n
s
u
m
er
-
o
wn
ed
h
i
s
to
r
i
ca
l
d
ata
ca
n
b
e
s
ee
n
in
T
ab
le
3
.
E
r
r
o
r
v
alu
e
s
ar
e
o
b
tain
ed
u
s
i
n
g
th
e
E
q
u
atio
n
6
g
e
n
er
ates
a
n
er
r
o
r
v
alu
e
o
f
5
2
7
,
2
9
8
3
.
I
n
T
ab
le
4
,
"
a
"
f
o
r
co
n
s
tan
t
s
,
"
b
1
"
is
c
on
s
tan
t
f
o
r
o
n
e
-
h
o
u
r
elec
tr
i
cit
y
co
n
s
u
m
p
tio
n
o
r
lo
ad
b
ef
o
r
e,
"
b
2
"
f
o
r
o
n
e
d
a
y
b
e
f
o
r
e,
―
b
3
‖
f
o
r
o
n
e
w
ee
k
b
e
f
o
r
e
th
e
lo
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o
b
tain
e
d
.
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s
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m
p
le
i
s
s
u
e
i
s
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v
a
n
ce
d
an
e
x
a
m
p
le
o
f
t
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e
p
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b
lem
f
o
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m
u
latio
n
.
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h
e
r
esear
ch
w
a
s
co
n
d
u
cted
u
s
in
g
a
6
4
-
b
it
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.
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h
e
d
ata
s
am
p
le
h
as
test
ed
a
to
tal
o
f
100
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ata.
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h
e
r
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le
is
f
o
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ed
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y
2
7
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le
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it
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m
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icie
n
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v
al
u
es
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n
ea
ch
r
u
le
th
at
is
f
o
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m
ed
as
f
o
llo
w
s
:
IF
Y
(
t
-
1
)
h
ig
h
A
ND
Y
(
t
-
2
)
h
ig
h
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ND
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-
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h
ig
h
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Y(t
-
4
)
h
ig
h
T
H
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z
=
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-
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Y(
t
-
2
)
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3
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Y(
t
-
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)
+ b
4
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t
-
4
)
.
Of
f
s
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r
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g
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tain
ed
f
r
o
m
1
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ai
n
s
.
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n
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q
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at
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n
7
,
th
e
i
n
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o
f
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h
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t
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o
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σ
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aised
i
n
th
e
r
an
g
e
[
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,
0
.
0
5
]
Valu
es
o
f
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(
0
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1
)
is
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an
d
o
m
n
u
m
b
er
.
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o
g
et
a
b
etter
f
it
n
e
s
s
,
a
co
n
s
ta
n
t
v
al
u
e
o
f
r
eg
r
ess
io
n
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n
s
m
ad
e
as
o
n
e
p
ar
en
t
s
o
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at
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e
E
S
c
an
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e
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s
e
to
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e
n
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etter
t
h
an
th
e
r
es
u
lt
s
o
f
th
e
r
eg
r
ess
io
n
ca
lcu
lat
io
n
.
T
h
e
ca
lcu
latio
n
o
f
th
e
v
al
u
e
o
f
ea
ch
c
o
ef
f
icie
n
t i
n
T
ab
le
4
.
T
ab
le
4
.
R
eg
r
ess
io
n
co
ef
icien
t
r
esu
lt u
s
i
n
g
1
0
0
d
ata
c
o
n
sta
n
ta
Co
e
ff
icie
n
ts
a
-
1
2
8
,
0
8
3
t
-
1
0
,
1
4
8
8
1
5
t
-
2
0
,
0
1
8
7
5
3
t
-
3
0
,
8
4
4
1
5
4
Vie
w
in
g
f
r
o
m
T
ab
le
4
ca
n
b
e
f
o
r
m
ed
f
o
llo
w
i
n
g
r
eg
r
ess
io
n
:
y
’=
−
2
0
0
5
5
0
0 5
0
0
5
5
T
h
is
v
alu
e
i
s
ap
p
lied
to
a
r
u
le
th
at
is
f
o
r
m
ed
to
s
er
v
e
as t
h
e
e
r
r
o
r
v
alu
e
to
s
ea
r
ch
f
o
r
f
it
n
e
s
s
.
E
q
u
atio
n
9
is
u
s
ed
to
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lcu
late
f
it
n
es
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(9
)
T
esti
n
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w
as
co
n
d
u
cted
u
s
in
g
1
0
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d
ata
to
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h
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ig
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ce
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est
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alu
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ed
in
th
is
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t
u
d
y
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P
o
p
s
ize
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v
alu
e
u
s
ed
in
th
is
s
t
u
d
y
i
s
7
λ
ac
co
r
d
in
g
to
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o
m
e
p
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u
s
s
tu
d
ie
s
[
3
6
]
C
o
m
b
i
n
atio
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tr
ials
u
s
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n
g
p
o
p
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izes
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ce
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ab
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5
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As
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ate
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h
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[
34
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,
[
3
6
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s
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ited
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e
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ize
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ig
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e
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v
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e
r
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lt
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g
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co
n
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g
en
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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I
J
E
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Vo
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4
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p
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u
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u
r
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5
.
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et
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ee
n
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d
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im
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ie
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h
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lt
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lc
u
latio
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p
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ize
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s
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h
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lt
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o
f
th
i
s
ca
lc
u
latio
n
i
s
f
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tn
es
s
as
in
T
ab
le
5
.
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h
e
r
esu
l
ts
o
f
th
e
f
o
r
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asti
n
g
ti
m
e
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er
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es
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al
y
s
is
ar
e
al
s
o
co
n
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ted
in
to
f
it
n
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s
.
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t
is
u
s
ed
to
m
a
k
e
it
ea
s
ier
to
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m
p
a
r
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th
e
f
i
n
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r
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lt
s
.
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h
e
r
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lt
s
o
f
th
is
ca
lc
u
latio
n
th
en
p
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in
Fi
g
u
r
e
5
.
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g
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r
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5
ca
n
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e
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t
h
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lt
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lt
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ten
d
r
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m
p
s
.
6.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
T
h
e
s
tu
d
y
co
m
p
ar
ed
t
h
e
r
es
u
lts
o
f
f
o
r
ec
asti
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s
in
g
"
ti
m
e
s
er
ies
An
al
y
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is
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h
o
u
r
p
r
o
p
o
s
ed
m
et
h
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d
,
elec
tr
icit
y
f
o
r
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asti
n
g
u
s
in
g
o
p
ti
m
izatio
n
f
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y
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u
g
e
n
o
o
n
t
h
e
co
ef
f
icie
n
t
r
u
le.
T
h
e
r
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lt
s
o
f
t
h
e
co
m
p
ar
is
o
n
ar
e
p
r
esen
ted
in
T
ab
le
6
.
E
v
o
lu
tio
n
S
tr
ateg
y
al
g
o
r
ith
m
is
o
n
e
o
f
th
e
h
e
u
r
is
tic
al
g
o
r
it
h
m
s
ca
n
f
in
d
a
b
etter
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
R
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Op
timiz
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f F
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(
Ga
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i D
w
i S
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n
tika
)
2249
s
o
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u
tio
n
an
d
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u
itab
le
f
o
r
o
p
tim
izatio
n
al
g
o
r
ith
m
[
3
5
]
.
E
lectr
icit
y
co
n
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m
p
tio
n
i
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t
h
e
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it
y
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g
u
r
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6
.
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h
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p
atter
n
is
b
ased
o
n
h
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s
to
r
ical
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ata
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r
o
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r
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r
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ted
b
y
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(
t)
,
y
(
t
-
1
)
,
y
(t
-
2
)
,
an
d
y
(
t
-
3
)
f
o
r
a
m
o
n
t
h
.
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e
s
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ts
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f
e
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m
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h
5
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ata
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ata
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n
Fig
u
r
e
7
.
Fo
r
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g
r
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lts
p
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v
ed
th
a
t
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r
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o
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r
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s
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y
p
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f
d
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[
3
6
]
.
T
ab
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.
C
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to
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h
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w
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in
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ab
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6
,
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ar
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n
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a
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n
iq
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m
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tr
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d
th
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s
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m
ea
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Fo
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m
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s
q
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R
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T
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MSE
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a
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h
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ap
p
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f
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tec
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th
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all
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m
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m
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s
p
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v
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y
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g
e
[
3
7
]
.
Her
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s
t
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f
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r
m
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R
MSE
:
R
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√
∑
(
−
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(1
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W
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T
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h
eu
r
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s
tic
m
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d
an
d
A
N
FIS
[
3
8
]
to
o
b
tain
lo
w
er
R
MSE
.
RE
F
E
R
E
NC
E
S
[
1
]
W
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P
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p
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|
El
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2
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)
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]
M
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s,
M
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&
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rm
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A
.
D.,
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