Scilab Function
Last update : 13/3/2006
ANN_NORM - Function to normalise data to train a feed forward network
Calling Sequence
-
[DATA_final,DATA_mean,DATA_std] = ANN_NORM(DATA,[OBJ,MULT,MEAN,STD])
Parameters
-
DATA
: Input data (matrix [PxN] where P is the number of input neurons and N the number of input patterns)
-
OBJ
: Objective of the normalisation
'raw2norm' = from raw values to normalised ones (default)'raw2norm' = from normalised values to raw ones
-
MULT
: Multiplicator of standard deviation to use for the normalisation process (default = 1). MULT can be a single value or a vector [Px1].
-
MEAN
: Means to use in the normalisation process (matrix [Px1])
-
STD
: Standard deviations to use in the normalisation process (matrix [Px1])
-
DATA_final
: DATA transformed by the normalisation
-
DATA_mean
: Means used to normalise DATA (vector [Px1])
-
DATA_std
: Standard deviations used to normalise DATA (vector [Px1])
Description
-
Add here a paragraph of the function description.
Other paragraph can be added
-
Add here a paragraph of the function description
Examples
// 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);
// Plot results
clf();
plot2d([TARGraw' ANN_NORM(OUT','norm2raw',1,m,s)]);
See Also
ANN_LMBR
,
ANN_JACOB
,
ANN_CONV_W
,
ANN_SIM
,
Authors
-
Julien Lerat
CEMAGREF Antony, HBAN Unit, julien.lerat@cemagref.fr