Download Back propagation-step-by-step procedure

Back propagation-step-by-step
procedure
G.Anuradha
Slides downloaded and sivanandem
• Step 1:
Normalize the I/p and o/ p with respect to
their maximum values.
For each training pair, assume that in
normalized form there are
‘l’ inputs given by {I} and ‘n’ outputs given by {O}
Step 2:
Assume the number of neurons in the hidden
layers lie between 1<m<21
• Step 3:
Let [V] be the weights of synapses connecting
input neuron and hidden neuron
Let [W] be the weights of synapses connecting
hidden neuron and output neuron.
Initialize the weights to small random values
usually form -1 to +1
[V]0=[random weights]
[W]0=[random weights]
Initialize change in weights to 0
• Step 4: Present the pattern as inputs to {I}.
Linear activation function is used as the
output of the input layer. {O}I={I}I
• Step 5: Compute the inputs to the hidden
layers by multiplying corresponding weights of
synapses as
{I}H=[V]T{O}I
• Step 6: The hidden layer units,evaluates the
output using either bipolar or unipolar
continuous activation function