Implementation of stockastic gradient backpropagation learning algorithm

For a list of all members of this type, see BackPropagationLearningAlgorithm Members.

System.Object
   LearningAlgorithm
      BackPropagationLearningAlgorithm

[Visual Basic]
Public Class BackPropagationLearningAlgorithm
   Inherits LearningAlgorithm
[C#]
public class BackPropagationLearningAlgorithm : LearningAlgorithm

Remarks

            
                                 PROPAGATION WAY IN NN
                               ------------------------->
            
                   o ----- Sj = f(WSj) ----> o ----- Si = f(WSi) ----> o
                 Neuron j                Neuron i                   Neuron k
               (layer L-1)               (layer L)                 (layer L+1)
            
            For the neuron i :
            -------------------
            W[i,j](n+1) = W[i,j](n) + alpha * Ai * Sj + gamma * ( W[i,j](n) - W[i,j](n-1) )
            T[i](n+1) = T[i](n) - alpha * Ai + gamma * ( T[i](n) - T[i](n-1) )
            
            	with :
            			Ai = f'(WSi) * (expected_output_i - si) for output layer
            			Ai = f'(WSi) * SUM( Ak * W[k,i] )       for others
            
            
NOTE : This is stockastic version of the algorithm because the error is back-propaged after every learning case. There is another version of this algorithm which works on global error.

Requirements

Namespace: NeuralNetwork Namespace

Assembly: NeuralNetwork.dll

See Also

BackPropagationLearningAlgorithm Members | NeuralNetwork Namespace