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- 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).