Introduction:- The hidden layer allows ANN to develop its own internal representation of input-output mapping. The complex internal representation capability allows the hierarchical network to learn any mapping and not just the linearly separable ones.
Algorithm for Training Network
• Basic algorithm loop structure
Initialize the weights
Repeat
For each training pattern
"Train on that pattern"
End
Until the error is acceptably low.
Back-Propagation Algorithm - Step-by-step procedure
Ø 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 x 1
§ n x 1
Ø Step 2 : Assume that the number of neurons in the hidden layers lie between 1 < m < 21
Ø Step 3 :Let [ V ] represents the weights of synapses connecting input neuron and hidden neuron
Let [ W ] represents the weights of synapses connecting hidden neuron and output neuron Initialize the weights to small random values usually from -1 to 1;
[ V ] 0 = [ random weights ]
[ W ] 0 = [ random weights ]
[ Δ V ] 0 = [ Δ W ] 0 = [ 0 ]
For general problems λ can be assumed as 1 and threshold value as 0.
Ø Step 4 : For training data, we need to present one set of inputs and outputs. Present the pattern as inputs to the input layer { I }I . then by using linear activation function, the output of the input layer may be evaluated as
Ø Step 5 : Compute the inputs to the hidden layers by multiplying corresponding weights of synapses as
Ø Step 6 :Let the hidden layer units, evaluate the output using the sigmoidal function as
Ø Step 7 : Calculate the error using the difference between the network output and the desired output as for the j th training set as