%LMNC Levenberg-Marquardt trained feed-forward neural net classifier% %  [W,HIST] = LMNC (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 initialization network mapping (default: [], meaning %           initialization 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 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 initialization. Use [] if the standard Matlab % initialization 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 Mathworks' Neural Network toolbox.%% SEE ALSO% MAPPINGS, DATASETS, BPXNC, NEURC, RNNC, RBNC, PRPROGRESS% 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: lmnc.m,v 1.16 2003/11/23 22:32:12 bob Exp $function [w,hist] = lmnc(varargin)	prtrace(mfilename);	[w,hist] = ffnc(mfilename,varargin{:});return