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RADIAL BASIS FUNCTION NEURAL
NETWORK DESIGN
(k )
r
enn
h1
w1
iq* (k )
h2
r (k 1)
w2
wq
r (k 2)
Input layer
+
-
rbf
hq
Hidden layer
Output layer
The multivariate Gaussian function was used
as the activated function for2 hidden layer:
hr exp(
X cr
2
2
r
), r 1,2,3,4,....q
RADIAL BASIS FUNCTION NEURAL
NETWORK DESIGN
(k )
r
enn
h1
w1
iq* (k )
r (k 1)
r (k 2)
Input layer
+
-
h2
w2
wq
hq
Hidden layer
Output layer
Output layer:
q
rbf wr hr
r 1
rbf
RADIAL BASIS FUNCTION
NEURAL NETWORK DESIGN
Based on the gradient descent method, the
learning algorithm of weights, node center and
variance were designed as follows:
wr (k 1) wr (k ) enn (k )hr (k )
X s (k ) crs (k )
crs (k 1) crs (k ) enn (k ) wr (k )hr (k )
2
r (k )
r (k 1) r (k ) enn (k ) wr (k )hr (k )
X ( k ) cr ( k )
(k )
3
r
2
RADIAL BASIS FUNCTION NEURAL
NETWORK DESIGN
Adjusting mechanism of fuzzy controller
q
cr1 iq* (k )
r 1
r2
cm,n (k ) e(k )( K p K i )d n,m wr hr
SIMULATION MODEL
VHDL codes of all system were embedded to
Modelsim block:
1: Neural Fuzzy controller
2: Current control and coordinately transforms
3: SVPWM
NEURAL FUZZY CONTROLLER
r* (k )
a0
+
x
a1
m (k 1)
m (k 2)
b1
b2
x
e( k 1)
-
x
m (k )
+
+
e(k )
-
x
r* (k 1)
r* (k 2)
a2
+
r (k )
+
s1
-
s3
s4
s6
s5
c j ,i
x
Ki
+
c j 1 ,i
d i , j 1
+
+
uf
x
+
x
x
dek
s9
s8
-
+
Fuzzification
iq* ( k )
ui
r (k )
+
r (k 1)
Neuro-1
computation
Defuzzification
s20
Neuro-3
computation
out2
rbf
di, j
x
+
+
s22
s23
s15
s24
s25
Computation of current
command
s26~s87
s16
r
c j ,i 1
x
+
Computation of RBF NN
s89
r
c j ,i
+
c j ,i 1
c j 1 ,i 1
d i 1 , j 1
c j 1 ,i 1
x
Kp
s88
c j ,i
Jaco
J3
d i 1 , j 1
Defuzzification
r
+
J2
d i , j 1
x
d i 1 , j
Ki
s21
d i 1 , j
s14
+
Neuro-2
computation
x
x
J1
out3
c j 1 ,i 1
s19
Ai 1 ( e )
Look-up fuzzy table
x
s18
B j 1 ( de )
+
B j (de )
+
s13
s12
d i 1, j 1
s17
1
s11
s10
di, j
x
-
RS,1
B j (de ) -
out1
ui
iq* ( k )
c j ,i 1
d i 1, j
B j (de )
B j (de )
de j 1
Ai (e )
1
Ai (e )
SD
s7
Kp
x
j
Ai (e )
RS,1
de(k )
+
Computation of speed error
and error change
Computation of reference model output
di, j
-
+
&
c j ,i
c j ,i 1
c j 1 ,i
c j 1 ,i 1
Look-up
Fuzzy rule
table
j&i
e( k 1)
x
s2
e i 1
ek
e(k )
s0
SD
-
i
α
e(k )
r
+
x
x
x
s90
s91
s92
s93
s94
r
c j 1 ,i
x
+
d i , j 1
s95
+
c j 1 ,i
s96
s97
s98
Tuning of fuzzy rule parameters
Implement by FSM (Finite state machine)
6
s99
Speed Control IC
NEURAL
ModelSim FUZZY CONTROLLER
*
r
Reference
Model
(RM)
Speed loop
m
+
uf
e
+
KI
1 Z 1
i
Fuzzy Controller
_
KP
r
de
Adjusting Mechanism
Jacobian
iq*
Z
1
Z 2
RBF
Neural Network
iq**
+q
Current
controlle
+
enn
PI
—
id* 0
v
PI
+
—
i
rbf
i
_
+
r
7
NEURAL FUZZY CONTROLLER
8