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using wavelet denoising as preprocessing function with real time data.

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Emiliano Rosso
Emiliano Rosso on 12 Apr 2017
When I train a neural network I need to process the training data X with multivariate wavelet denoising obtaining a new data set denoised X_den.
level = 4;
wname = 'sym2';
tptr = 'heursure';
sorh = 's';
mode = 'asym';
SCAL ='mln';
npc_app = 'none';
npc_fin = 'none';
[X_den, npc, nestco] = wmulden(X, level,wname,'mode',mode, npc_app, ...
npc_fin, tptr, sorh);
[mynet,tr]=train(mynet,X,Y);
After training I need to use 'mynet' to calculate the output of unknown data X(i).
output(i)=mynet(X(i));
Unknown data is obtained in realtime one by one and ,to be consistent with the trained network, I must denoise X(i) using the same Wavelet's parameters calculated previously.
But manual doesn't help me...
Thanks.

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