MATLAB: Backpropogation ,Multilayer perceptron,neural network

MATLAB

dear sir,
i m attaching a matlab code in which i tried to train the network using Feed forward Backpropogation.Here i m finding difficulty in in instruction
net_FFBP = createNet(inputsize, mimax, hneurons, fcnCELL, initflag, trainalgo, paramatrix, sameWEIGHT);
can i get how to save parameters in net_FFBP.I have attached the code below
function TrainingNet
load Feature.txt;    %load the features
FeatureS = Feature';    %Convert to column array
load Outtype.txt;     %load output type
OuttypeS = Outtype';
inputsize = size(FeatureS, 1);
min_data = min(min(FeatureS));
max_data = max(max(FeatureS));
mimax = [min_data max_data];
hneurons = 2000;
%initialize parameters for creating the MLP.
fcnCELL = {'logsig' 'logsig'};
initflag = [0 1];
trainalgo = 'gdm';
paramatrix = [10000 50 0.9 0.6]; % epochs = 100, show = 50, learning rate = 0.9, momentum term = 0.6 
sameWEIGHT = [];
net_FFBP = creteNet(inputsize, mimax, hneurons, fcnCELL, initflag, trainalgo, paramatrix, sameWEIGHT);
net_FFBP = newff(FeatureS, OuttypeS, 39);
[net_FFBP] = train(net_FFBP, FeatureS, OuttypeS);
save net_FFBP net_FFBP;
disp('Done: Training Network');

Best Answer

% function TrainingNet
% load Feature.txt; %load the features
% FeatureS = Feature'; %Convert to column array
% load Outtype.txt; %load output type
% OuttypeS = Outtype';
[I N ] = size(FeatureS)
[O N ] = size(Outtypes)
minmaxF = minmax(FeatureS) % Is a matrix [I 2]
Neq = N*O % Number of training equations
% I-H-O node topology
% Nw = (I+1)*H+(H+1)*O % Number of unknown weights
% Want Neq >> Nw or % H << Hub
Hub = (Neq-O)/(I+O+1) % Neq = Nw
r = 10 % Neq > r*Nw, ~2 < r < ~30
H = floor((Neq/r-O)/(I+O+1))
How did you get H = 2000 ???
% %initialize parameters for creating the MLP.
% fcnCELL = {'logsig' 'logsig'};
% initflag = [0 1];
What does initflag do?
% trainalgo = 'gdm';
% paramatrix = [10000 50 0.9 0.6]; % epochs = 100, show = 50,
100 or 10,000?
                                   % learning rate = 0.9, momentum term = 0.6
% sameWEIGHT = [];
I suggest first using the defaults in NEWFF
% net_FFBP = creteNet(inputsize, mimax, hneurons, fcnCELL, initflag, trainalgo, paramatrix, sameWEIGHT);
Is this supposed to be a replacement for NEWFF and net.Param.* ??
% net_FFBP = newff(FeatureS, OuttypeS, 39);
Now H = 39 ??
% [net_FFBP] = train(net_FFBP, FeatureS, OuttypeS);
% save net_FFBP net_FFBP;
% disp('Done: Training Network');
What is your question ??
Greg
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