MATLAB: Back Propagation Neural Network

back propagationDeep Learning Toolboxmatlab code for nnmlpneural network

Hi.

I need a workable Back Propagation NN code. My Inputs are 100X3 dimension and outputs are 100X2 dimension.Sample size is 100.

For example 1st 5 samples are inputs [-46 -69 -82; -46 -69 -82; -46 -69 -82; -46 -69 -82; -46 -69 -82;… ] and outputs are [0 0;2 1;5 5;4 3; 3 5;…].

Please suggest me if BP is suitable for my problem and what learning technique and activation function will be better to solve this problem? Do I need to apply generalization? Kindly help me with the matlab code if possible. Thank you very much.

Best Answer

Convert to matrices and transpose

[I N ] = size(inputs)
 [ O N ] = size(targets)

Use fitnet for regression and curve-fitting

 help fitnet
 doc fitnet

Use patternnet for classification and pattern-recognition

For examples beyond the help/doc documentation try searching with

 greg fitnet
 greg patternnet

in both the NEWSGROUP and ANSWERS.

Related Solutions

MATLAB: How to create a neural network with 1 layer only (no hidden layers)

 net = fitnet([]);     % For regression
 net = patternnet([]); % For classification

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Neural network toolbox – initialize the weights and biases with INITZERO:

 clear all, clc
[ X,T ]         = maglev_dataset;
net             = narxnet;
[ Xs Xi Ai Ts ] = preparets(net,X,{},T);
ts     = cell2mat(Ts);
MSE00s = mean(var(ts',1)) % 2.0021 Reference MSE
rng('default')            % For repeatibility
net = configure(net,Xs,Ts);
Wb = getwb(net);           % size(Wb) =[ 61 1]
Nw = net.numWeightElements % 61
net = setwb(net,zeros(Nw,1));
[ net tr Ys Es Xf Af ] = train(net,Xs,Ts,Xi, Ai);
% Ys = net(Xs,Xi,Ai); Es = gsubtract(net,Ts,Ys);
 NMSEs = mse(Es)/MSE00s   % 1 ==> useless net!
stopcrit = tr.stop        %  Minimum gradient
numepochs = tr.num_epochs % 2

% To see most of the result details use the command

% tr = tr (No semicolon)

 IW11=net.IW{1,1}  % zeros(10,2)

IW12 = net.IW{1,2} % zeros(10,2)
b11 = net.b{1,1}   % zeros(10,1)
b21 = net.b{2,1}   %   -0.095645
LW21 = net.LW{2,1} % zeros(1,10)