How to use ftinet to create neural network

Hi, I am doing a regression analysis using matlab Neuralnetwor for the verson2009, where I need to correlate 3 input data sets of(3*10) matrix with a target data set of (1*10)matrix. At first I have tried to use nntool and nftool. Now I am trying to do using command line by creating network with fitnet, but I am getting an error "??? Error using ==> load Unable to read file InputNeg: No such file or directory. Error in ==> math1 at 14 inputs = load ('InputNeg');" When I am trying to load the Input and Target Mat file. Then I tried to write the values directly in the code then I got the error "??? Undefined function or method 'fitnet' for input arguments of type 'double'. Error in ==> math1 at 17 net = fitnet(hiddenLayerSize);" Please If any one have any Idea about this, suggest me what should I do. Thanks in Advance

[Greg Heath]

post data and code

[Das Bably]

Sorry for late post. Here is my code in which I used the data sets directly a=[8.87 7.4 5.54 6.41 2.7 5.00 3.28 7.37 4.72 5.68]; b=[1.38 1.46 1.53 1.84 1.72 1.16 1.52 1.8 1.57 1.04]; c=[13.78 4.61 9.94 9.97 12.07 3.24 6.14 11.1 11.17 7.98]; y=[4.1644 3.5753 3.7534 4.1781 4.4589 2.8699 2.1781 4.0753 3.6644 2.3562]; inputs = vertcat(a,b,c); targets = y; % Create a Fitting Network hiddenLayerSize = 10; net = fitnet(hiddenLayerSize); % Set up Division of Data for Training, Validation, Testing net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Train the Network [net,tr] = train(net,inputs,targets); % Test the Network outputs = net(inputs); errors = gsubtract(outputs,targets); performance = perform(net,targets,outputs) % View the Network view(net) Thanks in advance

[Greg Heath]

Please format your code

Answer 1

[Greg Heath]

This is a poor example: N = 10 is not large enough to obtain robust practical solutions. With [I N ] =size(x) = [ 3 10 ], [O N ] = size(t) = [ 1 10], Ntst = round(0.15*N) = 2, Nval = Ntst =2, Ntrn = N-Nval-Ntst = 6, Ntrneq = Ntrn*O = 6 is the number of training equations.

With H hidden nodes, the number of unknown weights is Nw = (I+1)*H+(H+1)*O. A sufficient condition for a robust practical solution is Ntrneq >> Nw. However, Notice that, Ntrneq > Nw only when H <= Hub where Hub = -1+ceil( (Ntrneq-O) / (I+O+1)) = 0.

There is quite a bit more that I could say about this poor example. However, the main points are

 1. I was able to run your code
 2. Be careful of overfitting (H too large and/or N too small) with your real data set.

Hope this helps.

Thank you for formally accepting my answer

Greg