Backpropagation Program 

1. Purpose;
 a. Initialize a MLP using random initial weights
 b. Train a MLP network using backpropagation

2. Features;
 a. Train with or without momentum factor
 b. Train with or without batching (No simultaneous momentum and batching
 c. Adaptive learning factor
 d. Training MSE and error percentages are shown
 e. Feature importance can be measured
 f. Saves weights to disk

3. Example Run of Backpropagation Program
 a. Go to the "Batch Processing" option and press <ret>
 b. Observe the parameter file with commented keyboard responses;

grng.top            ! file storing network structure
2                   ! 1 to initialize with old weights, 2 for random weights
2                   ! 1 for coded output 2 for uncoded output
grng                ! specify filename for training patterns
0                   ! specify number of training patterns (0 for all)
0, 0                ! Enter numbers of 1st and last patterns to examine (0 0 for none)
2., 1.              ! hidden unit net function standard deviation and mean
1                   ! 0 for full batching, 1 for none
.007, .98           ! learning factor, momentum factor
50,  15.            ! Number of training iterations and error % threshold
4                   ! 1 to continue, 2 to change weights, 3 to process another file, 4 to stop
1                   ! 1 for feature selection, 0 else 
grng.wts            ! filename for saving the weights


    The program will read all patterns from the file grng, and train a MLP
    using the network structure file grng.top, which is shown below.

           3
          16          15           4
           1           1

    The network will have 3 layers including 16 inputs, 15 hidden units
    in one hidden layer, and 4 outputs (one per class). In addition,
    layers 2 and 3 connect to all previous layers. Training will stop
    after 50 iterations, or when the classification error % reaches 15 % .
    The final network weights will be stored in the file grng.wts. 
 c. Exit the DOS editor and observe the program running
 d. Go to the "Examine Program Output" option and press <ret>
 e. You can run this program on your own data, simply by editing the 
    parameter file in the "batch Run" option.



