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[
1
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.
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B
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DC
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-
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[
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1
1
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[
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I
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:
2
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8
8
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
4
,
Dec
em
b
er
2
0
1
7
:
1
7
0
5
–
1
7
1
3
1706
s
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lat
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n
.
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t
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8
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.
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v
is
ed
Heb
b
lea
r
n
in
g
r
u
les,
i
m
p
r
o
v
ed
Heb
b
lear
n
i
n
g
r
u
le
a
n
d
s
u
p
er
v
i
s
ed
Heb
b
lear
n
i
n
g
r
u
le
[
8
]
.
So
m
e
ti
m
es,
t
h
e
lear
n
i
n
g
th
eo
r
y
o
f
n
eu
r
al
n
et
w
o
r
k
al
g
o
r
ith
m
s
ta
k
e
a
lo
n
g
ti
m
e
to
ad
ap
t
th
e
w
ei
g
h
ts
o
f
S
NP
I
D
co
n
tr
o
l
w
h
e
n
t
h
e
s
y
s
te
m
ex
p
o
s
ed
to
an
y
d
is
t
u
r
b
an
ce
[
5
,
9
]
.
P
e
r
f
o
r
m
a
n
ce
ch
ar
ac
ter
is
tic
s
o
f
an
i
m
p
r
o
v
ed
SNP
I
D
co
n
t
r
o
ller
u
s
in
g
ad
d
itio
n
al
er
r
o
r
o
f
an
in
v
er
s
e
co
n
tr
o
l
s
ig
n
a
l
is
p
r
esen
ted
in
[
5
,
1
0
]
.
P
I
D
b
ased
o
n
a
s
in
g
le
ar
tif
ic
ial
Neu
r
al
Net
w
o
r
k
al
g
o
r
ith
m
f
o
r
in
t
ellig
e
n
t
s
e
n
s
o
r
s
ar
e
d
em
o
n
s
tr
ated
i
n
[
8
]
.
Sin
g
le
N
eu
r
o
n
P
I
D
co
n
tr
o
l o
f
air
cr
af
t d
eicin
g
f
l
u
id
s
r
ap
id
h
ea
ti
n
g
s
y
s
t
e
m
is
i
m
p
le
m
en
ted
in
[
9
]
.
Sin
g
le
Ne
u
r
o
n
ad
ap
tiv
e
P
I
D
c
o
n
tr
o
l
f
o
r
Hy
d
r
o
-
v
is
c
o
u
s
d
r
iv
e
cu
tc
h
ar
e
d
em
o
n
s
tr
ated
in
[
1
1
]
.
I
n
th
is
p
ap
er
th
e
GA
is
u
s
ed
to
f
in
d
th
e
o
p
ti
m
u
m
v
alu
e
s
o
f
SNP
I
D
co
n
tr
o
ller
p
ar
am
eter
s
b
as
ed
o
n
s
q
u
ar
e
er
r
o
r
o
b
j
ec
tiv
e
f
u
n
c
tio
n
[
7
]
.
T
h
e
f
u
zz
y
lo
g
ic
co
n
tr
o
l
s
y
s
te
m
b
ased
o
n
ex
p
er
t
k
n
o
w
led
g
e
d
atab
ase
h
as
less
ca
lcu
lat
io
n
s
in
tak
i
n
g
its
d
ec
is
io
n
a
n
d
s
u
itab
le
f
o
r
ap
p
licatio
n
s
w
h
er
e
p
r
o
ce
s
s
es
w
it
h
m
o
d
elin
g
d
if
f
ic
u
ltie
s
,
eith
er
b
ec
au
s
e
i
t
is
u
n
k
n
o
w
n
o
r
it
h
as
a
lo
t
o
f
ad
ju
s
tab
le
p
ar
a
m
eter
s
[
6
,
9
]
.
So
,
i
n
t
h
is
p
ap
er
th
e
S
NP
I
D
co
n
tr
o
l
is
co
m
b
i
n
ed
w
it
h
s
elf
-
t
u
n
in
g
f
u
zz
y
lo
g
ic
co
n
tr
o
l
to
in
tr
o
d
u
ce
a
n
o
v
el
m
et
h
o
d
to
ad
j
u
s
t
th
e
w
ei
g
h
ts
o
f
SNP
I
D
co
n
tr
o
l
ac
cu
r
atel
y
w
h
ic
h
m
ak
e
th
e
s
y
s
te
m
m
o
r
e
r
o
b
u
s
tn
es
s
a
g
ain
s
t
a
n
y
d
is
t
u
r
b
an
ce
s
.
T
h
e
m
a
in
co
n
tr
ib
u
tio
n
o
f
th
is
p
ap
er
d
esig
n
a
NFSNP
I
D
an
d
co
m
p
ar
i
n
g
i
t
w
ith
C
F
SNP
I
D
an
d
SNP
I
D
co
n
tr
o
ller
s
to
ac
h
ie
v
e
h
i
g
h
p
er
f
o
r
m
an
ce
B
L
D
C
m
o
to
r
d
r
iv
e
s
y
s
te
m
.
T
h
e
r
est
o
f
th
i
s
p
ap
er
is
ar
r
an
g
ed
a
s
f
o
llo
w
s
:
Sectio
n
I
I
p
r
esen
ts
th
e
d
y
n
a
m
ic
m
o
d
elin
g
o
f
B
L
DC
m
o
to
r
.
T
h
e
SNP
I
D
co
n
tr
o
l
tech
n
iq
u
e
s
ar
e
in
clu
d
ed
in
Sectio
n
I
I
I
.
Sectio
n
I
V
p
r
o
v
id
es
th
e
s
i
m
u
lat
io
n
r
es
u
lts
.
R
ec
en
t
l
y
,
S
ec
tio
n
V
co
n
cl
u
d
es.
2.
DYNA
M
I
C
M
O
DE
L
O
F
B
L
DC
M
O
T
O
R
T
h
e
tr
an
s
f
er
-
f
u
n
c
tio
n
b
ased
o
n
m
at
h
e
m
atica
l
m
o
d
els
ar
e
u
s
u
all
y
u
s
ed
in
au
to
m
atic
co
n
t
r
o
l
f
ield
s
.
So
m
e
co
n
tr
o
l
d
esig
n
an
d
an
al
y
s
i
s
m
et
h
o
d
s
,
s
u
c
h
as
th
e
r
o
o
t
-
lo
cu
s
m
et
h
o
d
an
d
th
e
f
r
eq
u
en
c
y
-
r
esp
o
n
s
e
m
et
h
o
d
ar
e
d
ev
elo
p
ed
b
ased
o
n
th
e
s
y
s
te
m
tr
an
s
f
er
f
u
n
ctio
n
[
1
2
]
.
T
h
e
tr
an
s
f
er
f
u
n
c
tio
n
o
f
t
h
e
B
L
D
C
m
o
to
r
at
n
o
lo
ad
m
a
y
b
e
w
r
i
tten
a
s
f
o
ll
o
w
s
[
1
2
]
:
(
)
=
ω
(
)
(
)
=
2
+
(
+
)
+
(
+
)
(
1
)
W
h
er
e
:
: D
C
v
o
lta
g
e.
: L
i
n
e
w
i
n
d
in
g
r
esis
ta
n
ce
.
: E
q
u
iv
ale
n
t li
n
e
w
i
n
d
in
g
in
d
u
ctan
ce
.
J
:
Mo
to
r
m
o
m
en
t o
f
in
er
t
ia.
ω
: M
o
to
r
r
o
t
o
r
s
p
ee
d
.
: V
is
co
u
s
co
n
s
ta
n
t.
: L
i
n
e
b
ac
k
-
E
MF
co
n
s
tan
t.
: L
i
n
e
to
r
q
u
e
co
n
s
ta
n
t.
T
h
e
B
L
DC
m
o
to
r
d
r
iv
e
s
y
s
te
m
i
s
d
e
m
o
n
s
tr
ated
as
b
lo
c
k
d
i
ag
r
a
m
in
Fi
g
u
r
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1
.
T
h
e
m
ai
n
c
o
m
p
o
n
en
t
s
o
f
d
r
iv
e
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te
m
co
n
tai
n
o
f
s
ix
s
tep
v
o
ltag
e
s
o
u
r
ce
in
v
er
ter
,
l
o
g
ic
cir
cu
it,
a
n
d
th
r
ee
h
all
e
f
f
e
ct
s
en
s
o
r
s
.
Fig
u
r
e
1
.
B
r
u
s
h
les
s
DC
m
o
to
r
d
r
iv
e
s
y
s
te
m
.
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I
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rict
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g
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ate
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r
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esp
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f
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ate
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to
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to
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u
r
e
3
s
h
o
w
s
t
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co
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r
esp
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d
in
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p
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r
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e
n
t
o
f
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m
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to
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t
ca
n
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r
th
at
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tar
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r
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n
t
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es
to
1
8
A
in
a
s
m
all
ti
m
e,
w
h
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t
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g
h
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e
0
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1
s
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p
h
as
e
cu
r
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en
t
w
ill b
e
in
cr
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s
ed
to
±
2
.
5
A
.
Fig
u
r
e
2
.
Op
en
lo
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p
r
esp
o
n
s
e
o
f
B
L
D
C
m
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to
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d
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s
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s
te
m
m
o
d
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Fig
u
r
e
3
.
T
h
e
co
r
r
esp
o
n
d
in
g
p
h
ase
c
u
r
r
en
t o
f
B
L
DC
m
o
to
r
m
o
d
el
3.
CO
NT
RO
L
T
E
CH
NI
Q
U
E
S
T
h
is
s
ec
tio
n
d
is
c
u
s
s
es
t
h
e
s
tr
u
ctu
r
e
o
f
t
h
r
ee
d
if
f
er
en
t
co
n
tr
o
l
tech
n
iq
u
es
b
ased
o
n
t
h
e
SNP
I
D
co
n
tr
o
l
w
h
er
e
th
e
f
ir
s
t c
o
n
tr
o
l te
ch
n
iq
u
e
u
s
es t
h
e
G
A
to
f
i
n
d
th
e
o
p
ti
m
u
m
p
ar
a
m
eter
s
o
f
SNP
I
D
co
n
tr
o
l [
5
]
,
w
h
i
le
th
e
s
ec
o
n
d
co
n
tr
o
l
tech
n
iq
u
e
is
t
h
e
s
el
f
-
t
u
n
i
n
g
f
u
zz
y
lo
g
ic
co
n
tr
o
l
to
u
p
d
ate
th
e
w
ei
g
h
ts
o
f
SNP
I
D
co
n
tr
o
l
p
r
o
p
o
s
ed
in
[
6
]
.
T
h
e
th
ir
d
o
n
e
is
a
n
e
w
h
y
b
r
id
co
n
tr
o
l
tech
n
iq
u
e
w
h
ich
co
m
b
in
e
s
t
h
e
SNP
I
D
co
n
tr
o
l
an
d
th
e
f
u
zz
y
P
I
D
co
n
tr
o
l.
T
h
e
co
n
tin
u
o
u
s
-
t
i
m
e
tr
ad
itio
n
al
P
I
D
r
ep
r
esen
t a
s
:
(
)
=
(
)
+
∫
(
)
0
+
(
2
)
W
h
er
e
u
(
t)
is
t
h
e
co
n
tr
o
ller
o
u
tp
u
t
a
n
d
e
is
t
h
e
co
n
tr
o
ller
er
r
o
r
.
T
h
e
d
is
cr
etiza
tio
n
ca
n
b
e
p
er
f
o
r
m
ed
b
y
d
i
f
f
er
en
tiat
in
g
b
o
th
s
id
es o
f
eq
(
2
)
as:
(
)
=
(
)
+
(
)
+
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
IJ
PEDS
Vo
l.
8
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No
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b
er
2
0
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7
:
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–
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7
1
3
1708
A
p
p
l
y
in
g
t
h
e
b
ac
k
w
ar
d
d
if
f
.
m
et
h
o
d
o
n
eq
(
3
)
g
iv
es
:
(
)
−
(
−
1
)
=
[
(
)
−
(
−
1
)
]
+
[
(
)
]
+
[
̇
(
)
−
̇
(
−
1
)
]
(
4
)
A
p
p
l
y
in
g
t
h
e
b
ac
k
w
ar
d
d
if
f
.
m
et
h
o
d
ag
ain
f
o
r
eq
(
4
)
:
(
)
−
(
−
1
)
=
[
(
)
−
(
−
1
)
]
+
[
(
)
]
+
[
(
)
−
(
−
1
)
]
−
[
(
−
1
)
−
(
−
2
)
)
]
(
5
)
So
lv
i
n
g
f
o
r
u
(
k
)
f
in
al
l
y
f
r
o
m
eq
(
5
)
g
iv
es th
e
d
is
cr
ete
ti
m
e
P
I
D
co
n
tr
o
ller
:
(
)
=
(
−
1
)
+
[
(
)
−
(
−
1
)
]
+
[
(
)
]
+
[
(
)
−
2
(
−
1
)
+
(
2
)
]
(
6
)
(
)
=
(
−
1
)
+
[
1
(
)
]
+
[
2
(
)
]
+
[
3
(
)
]
(
7
)
(
)
=
(
−
1
)
+
[
1
(
)
]
+
[
2
(
)
]
+
[
3
(
)
]
1
(
)
=
(
)
−
(
−
1
)
]
2
(
)
=
(
)
3
(
)
=
(
)
−
2
(
−
1
)
+
(
−
2
)
(
8
)
W
h
er
e
1
(
)
is
a
p
r
o
p
o
r
tio
n
al
er
r
o
r
,
2
(
)
is
an
i
n
te
g
r
al
er
r
o
r
an
d
3
(
)
is
ad
if
f
er
en
tial e
r
r
o
r
.
3
.
1
.
T
he
SNPID
C
o
ntr
o
ller
Sin
g
le
n
e
u
r
o
n
P
I
D
(
SNP
I
D)
is
o
n
e
o
f
t
h
e
s
i
m
p
le
s
t
n
eu
r
al
n
et
w
o
r
k
P
I
D
th
at
b
ea
d
s
o
n
o
n
l
y
o
n
e
n
eu
r
o
n
.
T
h
e
s
tr
u
ctu
r
e
o
f
SNP
I
D
co
n
tr
o
ller
is
illu
s
tr
ati
v
e
in
F
ig
u
r
e
4.
Fig
u
r
e
4
.
B
lo
ck
d
iag
r
a
m
o
f
S
NP
I
D
co
n
tr
o
l.
T
h
e
SNP
I
Dco
n
tr
o
ller
ca
n
b
e
e
x
p
r
ess
ed
as:
(
)
=
(
−
1
)
+
̅
̅
̅
(
)
(
)
(
9
)
̅
̅
̅
(
)
=
(
)
/
|
(
)
|
(
10)
T
h
e
co
n
tr
o
ller
o
u
tp
u
t
a
n
d
1
,
2
an
d
3
ar
e
th
e
n
e
u
r
o
n
w
eig
h
t
s
.
T
h
er
e
ar
e
v
ar
io
u
s
w
ei
g
h
ts
lear
n
i
n
g
alg
o
r
ith
m
s
b
ased
o
n
th
e
lear
n
in
g
t
h
eo
r
y
o
f
n
e
u
r
al
n
et
w
o
r
k
an
d
th
e
f
a
m
o
u
s
alg
o
r
ith
m
th
a
t
u
s
ed
in
th
i
s
w
o
r
k
is
s
u
p
er
v
is
ed
Heb
b
lear
n
i
n
g
r
u
l
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PEDS
I
SS
N:
2
0
8
8
-
8
694
A
N
o
ve
l F
u
z
z
y
S
elf
Tu
n
in
g
Te
ch
n
iq
u
e
o
f S
in
g
le
N
eu
r
o
n
P
I
D
…
(
M.A
.
A
b
d
el
Gh
a
n
y
)
1709
(
k
)
=
1
(
k
−
1
)
+
η
1
(
k
−
1
)
u
(
k
−
1
)
e
(
k
−
1
)
2
(
k
)
=
2
(
k
−
1
)
+
η
2
(
k
−
1
)
u
(
k
−
1
)
e
(
k
−
1
)
3
(
k
)
=
3
(
k
−
1
)
+
η
3
(
k
−
1
)
u
(
k
−
1
)
e
(
k
−
1
)
(
1
1
)
W
h
er
e
e
is
er
r
o
r
,
η
,
η
an
d
η
ar
e
p
r
o
p
o
r
tio
n
lear
n
in
g
s
p
ee
d
,
in
teg
r
al
lear
n
in
g
s
p
ee
d
an
d
d
if
f
er
en
ti
al
lear
n
in
g
s
p
ee
d
.
Gen
etic
al
g
o
r
ith
m
[
1
3
]
,
[
1
4
]
w
it
h
i
ts
m
ai
n
s
tep
s
(
r
ep
r
o
d
u
ctio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
)
i
s
ap
p
lied
to
o
b
tain
t
h
e
o
p
ti
m
al
v
al
u
es
o
f
th
e
f
o
u
r
p
ar
a
m
eter
s
t
h
at
ar
e
i
m
p
o
r
ta
n
t
in
d
esi
g
n
o
f
th
e
S
N
P
I
D
co
n
tr
o
l,
th
ese
p
ar
am
eter
s
ar
e
K
ca
lled
n
e
u
r
o
n
p
r
o
p
o
r
tio
n
co
ef
f
icie
n
t
an
d
t
h
e
t
h
r
ee
lear
n
i
n
g
s
p
ee
d
p
ar
am
eter
s
ar
e
η
,
η
an
d
η
.
T
h
e
u
s
ed
co
s
t f
u
n
ctio
n
a
s
s
h
o
w
n
in
(
1
2
)
m
in
i
m
ize
s
t
h
e
in
t
eg
r
ated
s
q
u
ar
e
er
r
o
r
e
(
t)
.
1
=
∫
(
(
)
)
2
∞
0
(
1
2
)
3
.
1
.
T
he
CF
SNPID
C
o
ntr
o
ller
T
o
en
h
an
ce
th
e
r
o
b
u
s
t
n
es
s
an
d
ad
ap
ta
b
ilit
y
o
f
th
e
SNP
I
D
co
n
tr
o
ller
,
th
e
f
u
zz
y
lo
g
ic
u
s
ed
to
d
esig
n
s
elf
-
t
u
n
in
g
SNP
I
D
co
n
tr
o
l.
T
h
e
lear
n
i
n
g
r
ate
s
ar
e
cr
itical
p
ar
am
eter
s
in
d
esi
g
n
t
h
e
SN
P
I
D
co
n
tr
o
l.
I
n
th
e
n
o
r
m
al
SNP
I
D
co
n
tr
o
l,
it
is
f
i
x
ed
an
d
t
h
e
w
ei
g
h
ted
co
ef
f
icien
ts
w
i
ll
i
n
cr
ea
s
e
o
r
d
ec
r
ea
s
e
in
t
h
e
s
a
m
e
p
r
o
p
o
r
tio
n
to
en
h
an
ce
t
h
e
p
er
f
o
r
m
a
n
ce
s
o
f
t
h
e
co
n
tr
o
ller
,
th
e
f
u
zz
y
lo
g
ic
e
m
p
lo
y
ed
to
d
y
n
a
m
icall
y
ad
j
u
s
t
th
e
p
r
o
p
o
r
tio
n
al,
in
teg
r
al
an
d
d
er
iv
ati
v
e
lear
n
in
g
r
ates.
T
h
e
s
el
f
-
tu
n
i
n
g
S
NP
I
D
co
n
tr
o
ller
s
tr
u
ctu
r
e
is
d
em
o
n
s
tr
ated
i
n
f
ig
u
r
e
5
[
6
]
.
Fig
u
r
e
5
.
B
lo
ck
d
iag
r
a
m
o
f
C
FS
NP
I
D
co
n
tr
o
ller
.
T
h
e
w
e
ig
h
ts
-
lear
n
i
n
g
al
g
o
r
ith
m
s
o
f
t
h
is
m
et
h
o
d
ar
e
s
u
p
er
v
is
ed
Heb
b
lear
n
in
g
r
u
les
as
s
h
o
w
n
i
n
eq
u
atio
n
s
1
1
an
d
th
e
ad
j
u
s
t
m
e
n
t o
f
t
h
e
lear
n
i
n
g
r
ate
ar
e
p
r
esen
ted
as f
o
llo
w
s
:
η
(
k
)
=
η
(
k
−
1
)
×
∆
η
(
k
)
η
(
k
)
=
η
(
k
−
1
)
×
∆
η
(
k
)
η
(
k
)
=
η
(
k
−
1
)
×
∆
η
(
k
)
(
1
3
)
W
h
er
e
∆
η
,
∆
η
an
d
∆
η
ar
e
th
e
o
u
tp
u
t
s
o
f
t
h
e
f
u
zz
y
co
n
tr
o
ller
.
B
o
th
e(
t)
an
d
∆e
(
t)
ca
n
b
e
s
ca
led
f
r
o
m
[
-
1
,
1
]
,
an
d
th
e
lin
g
u
i
s
tic
lab
els
ar
e
{Ne
g
ati
v
e
B
ig
,
Neg
ati
v
e
m
ed
iu
m
,
Ne
g
ati
v
e
s
m
all,
Z
er
o
,
P
o
s
itiv
e
s
m
all,
P
o
s
iti
v
e
m
e
d
iu
m
,
a
n
d
P
o
s
iti
v
e
B
ig
}
an
d
a
r
e
r
ef
er
r
ed
to
in
t
h
e
r
u
les
b
ases
as
{NB
,
NM
,
NS,
Z
E
,
P
S,P
M,
an
d
PB
}.
T
h
e
lin
g
u
i
s
tic
lab
els
o
f
t
h
e
o
u
tp
u
ts
ar
e
{Z
er
o
,
Me
d
iu
m
s
m
al
l,
S
m
all,
Me
d
i
u
m
,
B
ig
,
Me
d
iu
m
b
ig
,
a
n
d
v
er
y
b
ig
}
a
n
d
ar
e
r
ef
er
r
ed
to
i
n
t
h
e
r
u
les
b
ases
a
s
{
Z
,
MS,
S,
M,
B
,
MB,
an
d
VB
}.
Fig
u
r
es
6
an
d
9
s
h
o
w
th
e
m
e
m
b
er
s
h
ip
f
u
n
ctio
n
s
o
f
t
h
e
in
p
u
ts
a
n
d
th
e
o
u
tp
u
t
s
o
f
t
h
e
f
u
zz
y
lo
g
ic
co
n
tr
o
l.
T
h
e
d
ec
is
io
n
m
ak
in
g
lo
g
ic
s
i
m
u
late
s
th
e
h
u
m
a
n
d
ec
is
io
n
p
r
o
ce
s
s
.
T
h
e
r
u
le
b
ases
ar
e
s
i
m
p
li
f
ied
in
tab
les
2
,
3
an
d
4
.
T
h
e
in
p
u
t
e
h
as
7
lin
g
u
is
tic
l
ab
els
an
d
∆e
h
as
7
lin
g
u
is
t
ic
lab
els.
Hen
ce
,
th
er
e
ar
e
4
9
d
if
f
er
en
t
r
u
le
b
ases
.
I
n
th
i
s
p
ap
er
,
th
ese
4
9
r
u
le
b
ases
h
a
v
e
b
ee
n
s
i
m
p
li
f
ied
t
o
2
5
r
u
le
b
ases
as
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
4
,
Dec
em
b
er
2
0
1
7
:
1
7
0
5
–
1
7
1
3
1710
d
is
cu
s
s
ed
i
n
p
r
ev
io
u
s
w
o
r
k
i
n
[
1
5
]
.
T
h
e
s
i
m
p
lific
atio
n
d
etai
ls
ca
n
b
e
f
o
u
n
d
i
n
[
1
6
]
.
T
h
e
u
s
ed
d
ef
u
zz
if
icatio
n
m
et
h
o
d
is
w
e
ig
h
ted
av
er
a
g
e
m
eth
o
d
.
T
ab
l
e
2
.
T
h
e
R
u
le
b
ase
o
f
∆
η
.
∆
e
/
e
NB
NS
ZE
PS
PB
NB
VB
VB
VB
VB
VB
NS
B
B
B
MB
VB
ZE
ZE
ZE
MS
S
S
PS
B
B
B
MB
VB
PB
VB
VB
VB
VB
VB
T
ab
le
3
.
T
h
e
R
u
le
b
ase
o
f
∆
η
.
∆
e
/
e
NB
NS
ZE
PS
PB
NB
M
M
M
M
M
NS
S
S
S
S
S
ZE
MS
MS
ZE
MS
MS
PS
S
S
S
S
S
PB
M
M
M
M
M
T
ab
le
4
.
T
h
e
R
u
le
b
ase
o
f
∆
η
.
∆
e
/
e
NB
NS
ZE
PS
PB
NB
ZE
S
M
MB
VB
NS
S
B
MB
VB
VB
ZE
M
MB
MB
VB
VB
PS
B
VB
VB
VB
VB
PB
VB
VB
VB
VB
VB
Fig
u
r
e
6
.
Me
m
b
er
s
h
ip
s
f
u
n
c
ti
o
n
o
f
in
p
u
ts
(
e,
∆e
)
Fig
u
r
e
7
.
Me
m
b
er
s
h
ip
s
f
u
n
c
ti
o
n
s
o
f
o
u
tp
u
ts
(
∆
η
,
∆
η
an
d
∆
η
)
3
.
2
.
T
he
NF
SNPID
Co
ntr
o
ller
T
h
is
w
o
r
k
p
r
ese
n
ts
a
n
e
w
m
eth
o
d
o
f
SNP
I
D
co
n
tr
o
l.
I
n
t
h
is
t
h
e
m
et
h
o
d
,
th
e
f
u
zz
y
lo
g
ic
u
s
ed
to
u
p
d
ate
th
e
w
ei
g
h
ted
co
ef
f
ici
en
ts
w
h
er
e
d
y
n
a
m
ica
ll
y
ad
j
u
s
t
o
f
th
e
co
n
tr
o
ller
o
u
tp
u
t
ac
co
r
d
in
g
to
th
e
n
e
w
f
o
r
m
u
la
as
f
o
llo
w
in
g
:
(
)
=
(
(
1
(
)
)
+
(
2
∫
(
)
0
)
+
(
3
(
)
)
(
1
4
)
=
1
×
∆
=
1
×
∆
=
1
×
∆
W
h
er
e
∆
,
∆
an
d
∆
ar
e
th
e
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