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lyngby_nnr_main
(export/lyngby/lyngby_nnr_main.m)
Function Synopsis
[E, Y, U] = lyngby_nnr_main(x, T, arg1, arg2, arg3, arg4, ...
Help text
lyngby_nnr_main - Main function for Neural network Regression
function [E, Y, U] = lyngby_nnr_main(x, T, PropertyName, ...
PropertyValue)
Input: x Neural network input (the paradigm)
T Target output (the datamatrix)
PropertyName:
'GenOptim' { {Free} | EarlyStop |
HiddenUnitsEarlyStop | Pruning |
Pruning1DRegGridSearch |
Pruning2DRegGridSearch ]
Generalization optimization
'Validation' [ {SingleBlocked}]
How to compute the generalization
'Lag' { 7 } Time lag.
'HiddenUnits' { 3 } Number of hidden units, not
counting the threshold unit
'Reg' { 0.001 } Regularization parameter
(weight decay)
'InputType' [ {Direct} | SVDPreprocessed ]
'SingularValues' { 3 } Number of singular values
maintained (in the case of SVD
preprocessing), ie. the number of
input neurons
'Info' [ {0} | 1 ] Running information
about the optimization process
Output: E Error
Y Computed output
U Network weights
Neural network regression is a generalization (with respect to
non-linearity) compared to the FIR model.
See also: lyngby_nn_qmain, lyngby_fir_main
Cross-Reference Information
This function calls
This function is called by
Produced by mat2html on Wed Jul 29 15:43:40 2009
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