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- Functional Link Training and Testing Program
-
- Outline
-
- 1. Purpose;
- 2. Network Characteristics;
- 3. Files Needed or Produced
- 4. Example Run of Functional Link Program
- 5. Error Functions
-
-
-
- 1. Purpose;
- a. Initialize and train a functional link mapping network using
- a fast training method.
- b. Process a data file having no desired outputs.
- c. Non-demo version saves weights to a disk file.
-
- 2. Network Characteristics;
- a. Activation Functions; Linear 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, standard form, which means that
- each pattern or feature vector is followed by the desired outputs.
-
- 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;
-
- 1 ! train network
- 1 ! use old network structure
- GLS.tp ! old network structure filename
- GLS ! data filename
- 0 ! read all training data
- 1 ! examine some data
- 1 2 ! examine patterns 1 and 2 (training begins here)
- 3 ! stop
-
- The program will read all patterns from the file gls, and train a
- functional link net using the network structure file gls.tp, which
- is shown below.
- 3 4 1
- 35 1
-
- The network will be 3rd degree with 4 inputs and 1 output. The
- final network weights will not be stored 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
-
- Nout
- MSE = (1/Npat) SUM MSE(k) where
- k=1
-
- Npat 2
- MSE(k) = SUM [ Tpk - Opk ]
- p=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. MSE is printed
- for each iteration.
-
- b. Additional errors printed out are defined as follows.
- The rms error of the kth output, RMS(k), is SQRT( MSE(k)/Npat ),
- where SQRT means square root.
-
- The kth output's Relative RMS Error is
-
- R(k) = SQRT( MSE(k)/E(k) ) where
-
- Npat 2
- E(k) = SUM [ Opk-Mk ] and
- p=1
-
- Npat
- Mk = (1/Npat) SUM Opk
- p=1
-
- The kth output's Error Variance is MSE(k)/Npat.