Back-Propagation of Errors Algorithm

Each neuron has a continous output value between zero and one.

All       The units compute their values in two stages;
I. The net input to a given unit is computed as a linear sum of the output of each unit in the previous layer multiplied by the weight connecting the two units.

2. The actual output value is usually computed by the application of a sigmoid function;
This function has the advantage over linear threshold units in that it is differentiable at all points while still being able to behave as a decision unit.

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The idea is to get the network to learn a set of weights that can associate a set of input vectors with a set of output vectors in an initial training period so that when the network sees an input vector at some later date it will produce the associated output vector.

Initially weights are set randomly and a poor mapping is provided.

Backpropagation is an iterative procedure which gradually adjusts the weight values until an adequate mapping is provided.

To determine the current error of the network we compute the error of each output unit in the network across all pattern couples of the network.