[2] This type of artificial neural network (ANN) was derived from the Bayesian network[3] and a statistical algorithm called Kernel Fisher discriminant analysis.
The second layer sums the contribution for each class of inputs and produces its net output as a vector of probabilities.
Finally, a compete transfer function on the output of the second layer picks the maximum of these probabilities, and produces a 1 (positive identification) for that class and a 0 (negative identification) for non-targeted classes.
A hidden neuron computes the Euclidean distance of the test case from the neuron's center point and then applies the radial basis function kernel using the sigma values.
The output layer compares the weighted votes for each target category accumulated in the pattern layer and uses the largest vote to predict the target category.