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if
0
)
(w lm )
E
(
t
1
)
(w lm ) =
E
(
t
×
then
sign
)
(w lm )
E
(
t
{ (w lm (
t
)) =−
( ij (
t
))
(w lm (
t
+
1
)) = (w lm (
t
)) + (w lm (
t
)) }
For Imaginary part of weight:
if
0 then
)
(w lm )
(
)
(w lm ) >
(
E
t
1
E
t
×
)) × μ + , ( max ))
{ ( lm (
t
)) =
min
( ( lm (
t
1
sign
)
(w lm )
E
(
t
(w lm (
t
)) =−
( ij (
t
))
(w lm (
t
+
1
)) = (w lm (
t
)) + (w lm (
t
)) }}
if
0 then
)
(w lm )
E
(
t
1
)
(w lm ) <
E
(
t
×
)) × μ , ( min ))
{ ( lm (
t
)) =
max
( ( lm (
t
1
if
(
E
(
t
)>
E
(
t
1
))
then
)
(w lm ) =
E
(
t
(w lm (
t
+
1
)) = (w lm (
t
)) (w lm (
t
1
))
and
0
}
if
= 0
{ (w lm ( t )) =− sign
)
(w lm )
E
(
t
1
E ( t )
(w lm )
)
(w lm )
E
(
t
×
then
( ij ( t ))
(w lm ( t + 1 )) = (w lm ( t )) + (w lm ( t )) }}
t
=
t
+
1 }
Until(converged)
The update values and the weights are changed every time when a new training
set is presented. All update values (
lm ) are initialized to
0 . The initial update
value,
0 , is selected in a reasonably proportion to the size of initial weights. In
order to prevent the weights from becoming too small and too large, the range of
update value has been restricted to a minimum limit (
min ) and maximum limit
(
max ). In experiments, it had been throughly seen that by setting these update value
quiet small, one could obtain a smooth learning process. The choice of decrement
factor
μ =
μ + =
2 generally yields good results. It was
also observed that small variation in these values did neither improve nor deteriorate
learning process.
0
.
5 and increment factor
1
.
 
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