ffnc
FFNC
Feedforward neural net classifier backend
[W,HIST,UNITS] = FFNC(ALG,A,UNITS,ITER,W_INI,T,FID)
Input  ALG  Training algorithm: 'bpxnc' for backpropagation, 'lmnc' for LevenbergMarquardt  A  Training dataset  UNITS  Array indicating number of units in each hidden layer. Default is a single hidden layer. Its size is the half of the number of objects in A divided by feature size plus class size (roughly half of the number of parameters to be optimised) with a maximum of 100;  ITER  Number of iterations to train (default: inf)  W_INI  Weight initialisation network mapping (default: [], meaning initialisation by Matlab's neural network toolbox)  T  Tuning set (default: [], meaning use A)  FID  File ID to write progress to (default [], see PRPROGRESS) 
Output  W  Trained feedforward neural network mapping  HIST  Progress report (see below) 
Description This function should not be called directly, but through one of its frontends, BPXNC or LMNC. Uses the Mathworks' Neural Network toolbox.
This routine escapes to KNNC if any class has less than 3 objects. See also
mappings, datasets, bpxnc, lmnc, neurc, rnnc, rbnc, knnc, This file has been automatically generated. If badly readable, use the helpcommand in Matlab. 
