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Backpercolation 1 (Perc1) is a learning algorithm for feedforward networks. Here the weights are not changed according to the error of the output layer as in backpropagation, but according to a unit error that is computed separately for each unit. This effectively reduces the amount of training cycles needed.

The algorithm consists of five steps:

  1. A pattern is propagated through the network and the global error is computed.

  2. The gradient is computed and propagated back through the hidden layers as in backpropagation.

  3. The error in the activation of each hidden neuron is computed. This error specifies the value by which the output of this neuron has to change in order to minimize the global error Err.

  4. All weight parameters are changed according to .

  5. If necessary, an adaptation of the error magnifying parameter is performed once every learning epoch.

The third step is divided into two phases: First each neuron receives a message , specifying the proposed change in the activation of the neuron (message creation - MCR). Then each neuron combines the incoming messages to an optimal compromise, the internal error of the neuron (message optimization - MOP). The MCR phase is performed in forward direction (from input to output), the MOP phase backwards.

The internal error of the output units is defined as , where is the global error magnification parameter.

Unlike backpropagation Perc1 does not have a learning parameter. Instead it has an error magnification parameter . This parameter may be adapted after each epoch, if the total mean error of the network falls below the threshold value .

When using backpercolation with a network in SNNS the initialization function Random_Weights_Perc and the activation function Act_TanH_Xdiv2 should be used.
Tue Nov 28 10:30:44 MET 1995