Biomedical Engineering Reference
In-Depth Information
Fig. 7
Training results ANN-BPN
5. Correct the weights using Newton's steepest descent technique.
6. If p \ number training sets P, set p = p ? 1 and go to step-3.
7. If
P
p
j
E
p
j
2
[ tolerance
, increment the iteration index n and go to step-2.
p
¼
1
The above method explained is tested successfully and requires the input and
output to be from a continuous domain. Moreover, the input and the output set of
vectors are non-contradictory for a successful training and operational function.
The following section explains the use of ANN-BPN to determine the voltages
of a radial distribution system.
All paragraphs must be indented. All paragraphs must be justified, i.e. both left-
justified and right-justified.
ANN-BPN Implementation for Determining Load Flow
Solution
The input vector for the ANN-BPN is the reap power and reactive power loads at
various buses of power system. Therefore,
X
¼
P
1
;
P
2
;
P
3
;
.........P
n
; Q
1
;
Q
2
;
Q
3
.........Q
n
½
ð
6
Þ
here, P
i
and Q
i
are the real and reactive power loads at the ith bus of the RDS
and 'n' is the number of buses in the RDS.
By using this scheme several sets of loads were created:
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