Information Technology Reference
In-Depth Information
2
g
The EKF has been implemented with the assumption that Q
ð
n
Þ ¼
I n ; n r
and r
˃ 2 .
The MRAN prediction algorithm Sundararajan et al. ( 2002 ), Yingwei et al.
( 1998 ), with the EKF, here called MRANEKF algorithm, is shown in Fig. 9 and it
is summarized as follow:
(n)=
ð
ð
Þ;
ð
ÞÞ
1. For each observation
x
n
y
n
do: compute the overall network output:
ð n ÞÞ ¼ k 0 þ P i¼1 k i /
y ð n Þ ¼f ð
x
ð
k
x
ð n Þ
c i
k
Þ
where K is the number of hidden
units;
2. Calculate the parameters required by the growth criterion:
kk ¼
ð
k
ð
Þ
ð
ð
ÞÞ
k
￿
e
n
y
n
f
x
n
s
P
n
2
e ð j Þ
e rms ð
Þ ¼
￿
n
M
j¼n ð M 1 Þ
d
ð
n
Þ ¼
k
x
ð
n
Þ
c r ð
n
Þ
k
￿
Fig. 9 Flow chart of the
MRANEKF algorithm
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