Noisy results from Neural Network
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I've trained a feed-forward neural network with 4 inputs and 2 targets with 10000 sample data. When I use this network for testing on a set of data, the average of data seems acceptable, but it is very noisy (see attached figure). Any suggestion to resolve this issue? I have tried many different layer and neuron numbers, as well as training methods, but no major improvement.
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Greg Heath
2014-8-10
Try preprocessing with a lowpass filter.
The cheapest one I can think of is
x(i) = mean([x0(i-1),x0(i),x0(i+1)]) % 3-point moving average
I don't necessarily recommend it, it is just an example. Better to find a good LPF reference.
Hope this helps.
Thank you for formally accepting my answer
Greg
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Greg Heath
2014-8-19
Sorry, my response assumed the input data was one-dimensional.
I think you can better understand your results if you do the following for each input
[sortx1 ind1] = sort(input(1,:));
figure, hold on plot(sortx1,target(ind1),'k--') plot(sortx1,output(ind1),'b')
Hope this helps,
Greg
Greg Heath
2014-8-19
It doesn't appear to me that the output is significantly more noisy than the target.
Maybe you can see it better with other plotting options or plotting error (target-output).
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Sam136
2014-8-20
编辑:Sam136
2014-8-20
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Greg Heath
2014-8-20
If you are going to switch to narxnet. See my posts on how to choose delays
greg nncorr
how to choose number of hidden nodes
greg Hub
and how to normalize results
greg NMSE
greg R2
greg R2a
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