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Fig. 10 Input Output structure of the PV production forecast neural network
3. Apply the criterion for adding a new hidden unit:
if
‖
e(n)
‖
> E
1
and e
rms
(n)>E
2
and d(n)>E
3
allocate a new hidden unit K +1
with:
ʻ
K+1
= e(n)
c
K
þ
1
¼
x
ð
n
Þ
b
K
þ
1
¼ a
k
x
ð
n
Þ
c
r
ð
n
Þ
k
else
tune the network parameters:
w
ð
n
Þ
¼
w
ð
n
1
Þþ
k
ð
n
Þ
e
ð
n
Þ
update the error covariance matrix:
P
T
P
ð
n
Þ
¼
I
k
ð
n
Þ
a
ð
n
Þ
ð
n
1
Þþ
Q
ð
n
1
Þ
end
4. Check the criterion to prune hidden units:
compute the hidden unit outputs:
o
i
ð
n
Þ
¼k
i
/
ð
k
x
ð
n
Þ
c
i
k
Þ;
i
¼
1
; ...;
K
compute the normalized outputs:
o
i
ð
n
Þ
o
i
ð
n
Þ
¼
Þg
;
i
¼
1
; ...;
K
f
o
i
ð
max
n
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