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MLPC.HLP
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1994-09-06
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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).