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output, so BP network has been widely applied in pattern
recognition, function approximation and other areas [5].
The three nodes of the BP network is represented as:
x
j
input node , hidden node
y σ
i , output node
j , network
w
ij
weight of input node , 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.
e
j
( n) = d ( n) − O ( n)
j
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.
j
Begin
(6)
Input layer Hidden layer Output layer
Figure 1. 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
y
the i (i=0,set
l −1
( )
then
then
l−1
0
= −
is the signal transmitted from
l
l
1 w
j0
( n) = θ
j
( n)
,
) unit of the
layer.
If the j unit activation function is sigmoid function,
y
1
() l
j
( n) =
() l
1+
exp − v ( n)
( )
and
∂y
f ′( v
j
( n)
) = = y
j
1
∂v
() l
j
( n)
( n)
j
j
[ ]
( n) − y ( n)
j
(2)
(3)
l = 1 ,
If the j unit belongs to the first hidden layer ( )
( 0) y
j
= x
j
( n)
( )
If the j unit belongs to the output layer l = L
( L
y )
( n) O ( n)
j
=
j
(4)
, then
(5)
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 PROCESS
Experiment was carried out in the Matlab7.6
environment. Acquisition of a high-quality database of
physiological signals is vital for the emotion recognition
algorithm development. An important concern is the
selection of signals that are to be used as input to the
emotion recognition system. It is desirable that the
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