Outline

1. Multilayer Perceptron (MLP) Options
2. Error Functions



1. Multilayer Perceptron (MLP) Options
 a. Train an MLP using backpropagation (BP). Batching, which 
    denotes the accumulation of weight changes over portions of the 
    training set before they are used, is an option. The learning 
    factor changes adaptively. 
 b. Fast training of MLP networks. Trains networks one or two 
    orders of magnitude faster than BP.
 c. Analyze and prune trained MLPs. The non-demo version produces weight
    and network structure files for the pruned network.
 d. Process data using a trained MLP. Data may or may not include 
    desired outputs.
 e. Create MLP subroutine. Given a network structure file and a 
    weight file, creates an MLP subroutine in Fortran with a parameter 
    list that includes only the input array and output array.
 f. Create formatted weight file. Given a network structure file and a 
    weight file, creates a formatted weight file that clearly shows 
    the different connections and their weights and thresholds.

2. Error Functions

 a. The error function that is being minimized during backpropagation
    training and fast training is

                   Npat Nout             2
    MSE = (1/Npat) SUM  SUM [ Tpk - Opk ]
                   p=1  k=1  

    where Npat is the number of training patterns, Nout is the number 
    of network output nodes, Tpk is the desired output for the pth
    training pattern and the kth output, and Opk is the actual output
    for the pth training pattern and the kth output. The desired
    output Tpk is 0 for the correct class and 1 for other classes.
    MSE is printed for each iteration.

 b. The error percentage that is printed out during training is

    Err = 100 x (number of patterns misclassified/Npat).
