Functional Link Training and Testing Program

Outline

1. Program Purpose;
2. Network Characteristics;
3. Files Needed or Produced;
4. Example Run of Functional Link Program
5. Error Functions




1. Program Purpose;
 a. Initialize a functional link network
 b. Train the network using a fast training method
 c. Process data files using the network

2. Network Characteristics;
 a. Activation Functions; Sigmoidal ( Out = 1/(1 + exp(-Net)) ) output 
        units 
 b. Net Functions; polynomial functions of the inputs, with 
        user-chosen degrees of 1 to 5.

3. Files Needed or Produced;
 a. The network structure file; stores the number of inputs and outputs, 
        and the polynomial degree.
 b. The training or testing data file, which gives example inputs 
     and outputs for network learning, or for testing after learning.
     Al data files are in formatted, IANS form, which means that 
     each pattern or feature vector is followed by the correct class
     number.
 c. The non-demo version saves weights.

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

5         ! # of training iterations
2         ! 1 for old net structure file, 2 for new one
2 16      ! degree and number of inputs
4         ! # of outputs (one per class)
Grng.tp   ! network structure filename
2         ! outputs not coded (1 per class)
Grng      ! data filename
0         ! # of patterns to read (0 to read all training data)
1         ! examine some data
1 2       ! examine patterns 1 and 2 (training begins here)
4         ! stop

    The program will read all patterns from the file grng, and train a
    functional link net using the network structure file grng.tp, which
    is shown below.

           2          16           4
         153           4

    The network will be 2rd degree with 16 inputs and 4 outputs
    (one for each of the four classes). The final network weights
    will not be saved in the demo version.
 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.

5. Error Functions

 a. The error function that is being minimized during functional link
    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).
