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x
j
input node , hidden node
y σ
i , output node
j , network
Begin
weight of input node
w
ij , network weight of hidden node
T
and output node
li , the expectation output the output
t
node l , Figure 1 shows the BP neural network structure.
The basic learning algorithm of BP neural network:
l) Determine the various learning parameters on the
basis of the known network structure, including input
layer, hidden layer, output layer neuron number, learning
rate and error parameters.
2) Initialize the network weights and thresholds.
3) Provide learning sample: input vector and target
vector.
4) Start to learn, and do the following for each sample:
1 Forward-calculation of the j unit in the l layer:
T
() l
l l−1
v
j
( n) = ∑ w
ji
( n) yi
( n)
i=
0
(1)
y l −1
( n)
Equation (7)
i
the i (i=0, set
l −1
( )
then
y
l−1
0
= −
1
is the signal transmitted from
l
l
w
j0
( n) = θ
j
( n)
,
) unit of the
layer.
If function of the j unit activation is sigmoid function,
And
y
1
() l
j
( n) =
() l
1+
exp − v ( n)
( )
() l
∂y
j
( n)
f ′( v
j
( n)
) = = y
j
( n) [ 1−
y
j
( n)
]
∂v
j
( n)
( )
If the j unit belongs to the first hidden layer l = 1
( 0) y
j
= x
j
( n)
( )
If the j unit belongs to the output layer l = L
( L
y )
j
( n) = O
j
( n)
e ( n) = d ( n) − O ( n)
j
j
j
j
, then
(2)
(3)
, then
2 back-calculation of δ :
For the output units,
() l
( ) ( L
δ n e ) ( n ) O ( n ) [ O ( n )
j
=
j j
1−
j
]
(7)
and for the hidden layer units,
() l
( ) () l
( ) [ () l
( ) ( l+
1
n y n y n ] )
( n) w
( l+
1
δ
) j
=
j
1−
j ∑δ
k kj
( n)
k
(8)
3 Fix the right values according to the following:
() l
( ) () l
( ) ( l ) ( ) ( l−1
w n w n n y
)( n )
jk
+ 1 =
ji
+ ηδ
j i
(9)
5) Enter a new sample until it reaches the error
requirement, and the input order of each cycle in training
samples needs a re-random order.
The specific program flow chart of training network
using BP algorithm shows as fig.2.
(4)
(5)
(6)
NO
Network initiation
Initialize learning sample
Compute network layer output of each neuro neuron
Compute the train error
Modify the network weight
Meet the error
precision
End
YES
Figure 2. BP algorithm Flow Chart
III. EXPERIMENT RESEARCH
Experiment was carried out in the Matlab7.6
environment. The physiological signal data of EMG is
from the Augsburg University in Germany, it is four kinds
of emotions, joy, anger, sadness and pleasure, generated
by a subject's conduct of music by Johannes Wagner and
Figure 3. The EMG joy signal of wavelet transform in six scales
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