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fact >> (X->y) Non-linear calibrations > nns_init

nns_init

initialization of a neural network with 1 hidden layer, with bias

Calling sequence

[wh,wo]=nns_init(x,y,(options))

Paramètres

x:

calibration dataset

x is a matrix of dimensions (n x q) or a Div structure

y:

reference values, to be predicted

y is a matrix of dimensions (n x no) or a Div structure

(nh):

number of hidden neurons (by default = 5)

(method):

method of initialization

method='l': linear model for the first neuron, random small value for the following

method='n': Nguyen-Widrow (by default)

method='r': random values of small variance

wh:

coefficients of the hidden neurons + bias

wh is a matrix of dimensions ((q+1) x nh) or a Div structure

nh is the number of hidden neurons

wo:

coefficients of the output neurons + bias

wo is a matrix of dimensions ((nh+1) x no) or a Div structure

no is the number of output neurons

Examples

[wh,wo]=nns_init(x,y,4)

Bibliography

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