Neural Network tool nftool

2013 7 11
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1. Once a network is trained how can it be used to find new outputs from known inputs?
2. There is a problem ....When I use nftool with some data set (having 5-dimensional inputs and 4-dimensional outputs) It fit very well. nftool takes 1000 iteration for it. and performance is very well around e-02..... But When I use advanced script generated by nftool, or When I write my own code for the same. It takes 12-20 iteration and performance is as high as e+06 to e+07.
Where is the problem?
Please help !

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Greg Heath
Greg Heath 2013-7-12
Not enough info.
It would help if you tried your code on one of the nndatasets
help nndatasets
and post relevant parts of code and all error messages.
sandeep
sandeep 2013-7-12
When I tried the code with nndatasets....There is no problem. But When I used the same code with my datasets. This problem arises. Even the code generated by the nftool (Advanced scripts) is not working. My code is this. inputs=[] (Here I have given my input matrix) outputs=[] (Here I have given my output matrix) net=feedforwardnet; net=configure(net,inputs,Outputs); view(net) [net,tr]=train(net,inputs,Outputs);
P.S. There is no error messages, program is running but takes very few iteration and performance is around e+06 or e+07.
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Greg Heath
Greg Heath 2013-7-13

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Not enough information.
1. Your spelling of outputs is inconsistent
2. [ I N ] = size(inputs) =?
3 [ O N ] = size(outputs) = ?
4. MSE00 = mean(var(outputs'),1) = ? % Reference MSE
5. Ntrn = N - 2*(0.15*N) % Default No. of training examples
6. Ntrneq = Ntrn*O % No. of training equations
7. H = 10 % default No. of hidden nodes
8. Nw = (I+1)*H+(H+1)*O % No. of unknown weights to be estimated from Ntrneq
9. In order to avoid a poor set of initial random weights, obtain Ntrials = 10
separate designs by looping over i = 1:Ntrials
10. Pick the best design with the lowest normalized VALIDATION MSE
NMSEval = tr.best_vperf/MSE00 % Desire 0 < NMSEval << 1
Hope this helps.
Thank you for formally accepting my answer
Greg
P.S. Search on greg i=1:Ntrials

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