ANN_NORM — Function to normalise data to train a feed forward network
[DATA_final,DATA_mean,DATA_std] = ANN_NORM(DATA,[OBJ,MULT,MEAN,STD])
Input data (matrix [PxN] where P is the number of input neurons and N the number of input patterns)
Objective of the normalisation. 'raw2norm' = from raw values to normalised ones (default), 'norm2raw' = from normalised values to raw ones.
Multiplicator of standard deviation to use for the normalisation (default = 1). MULT can be a single value or a [Px1] vector.
Means to use in the normalisation process (matrix [Px1])
Standard deviations to use in the normalisation process (matrix [Px1])
DATA transformed by the normaliation of the denormalisation process
Means used to normalise DATA (vector [Px1])
Standard deviations used to normalise DATA (vector [Px1])
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// Raw values INraw = 200*rand(6,100)-50; TARGraw = 1000*rand(1,100)-888; // Calibration of a network with 6 input nodes, 4 nodes in the hidden layer and 1 output node IN = ANN_NORM(INraw); [TARG,m,s] = ANN_NORM(TARGraw); Wini = rand(4,7,2); [W,OUT,RMSE] = ANN_LMBR(IN,TARG,4,Wini,'def','def',%f); // %f => plot not shown // Plot results xset('window',0); plot([TARGraw' ANN_NORM(OUT','norm2raw',1,m,s)]);