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

nns_simul

predictions by a neural network with 1 hidden layer and with bias

Calling sequence

[ypred]=nns_simul(wh,wo,x)

Arguments

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 de sortie + bias

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

no is the number of output neurons

x:

calibration dataset

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

ypred:

predictions obtained from the neural network (wh,wo) applied to x

ypred is a Div structure

ypred.d is a matrix of dimensions (n x no)

Examples

[ypred]=nns_simul(wh,wo,x)

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