%BPXNC Back-propagation trained feed-forward neural net classifier% %   [W,HIST] = BPXNC (A,UNITS,ITER,W_INI,T,FID)%% INPUT%   A      Dataset%   UNITS  Array indicating number of units in each hidden layer (default: [5])%   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 descriptor to report progress to (default: 0, no report)%% OUTPUT%   W      Trained feed-forward neural network mapping%   HIST   Progress report (see below)%% DESCRIPTION % A feed-forward neural network classifier with length(N) hidden layers with % N(I) units in layer I is computed for the dataset A. Training is stopped % after ITER epochs (at least 50) or if the iteration number exceeds twice % that of the best classification result. This is measured by the labeled % tuning set T. If no tuning set is supplied A is used. W_INI is used, if % given, as network initialisation. Use [] if the standard Matlab % initialisation is desired. Progress is reported in file FID (default 0). %% The entire training sequence is returned in HIST (number of epochs, % classification error on A, classification error on T, MSE on A, MSE on T).% % Uses the Mathwork's Neural Network toolbox.%% SEE ALSO% MAPPINGS, DATASETS, LMNC, NEURC, RNNC, RBNC% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: bpxnc.m,v 1.2 2006/03/08 22:06:58 duin Exp $function [w,hist] = bpxnc(varargin)	prtrace(mfilename);	[w,hist] = ffnc(mfilename,varargin{:});	return