<< nns_simul (X->y) Non-linear calibrations nns_simulter >>

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

nns_simulbis

prediction and prediction errors of a neural network with 1 hidden layer and with bias

Calling sequence

[ypred,std_ypred]=nns_simulbis(wh,wo,x,std_res,cov_w)

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 + 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

std_res:

estimation of the standard deviation of the residuals

std_res is a vector of dimensions (1 x no) or a Div structure

cov_w:

variance-covariance matrix of the weights

cov_w is a matrix of dimensions (nw x nw) or a Div structure

nw=(q+1)*nh + (nh+1)*no

ypred:

predictions yielded by the neural network (wh,wo) applied to x

ypred is a Div structure Div

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

std_ypred:

estimation of the standard errors associated to each prediction

std_ypred is a Div structure

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

Examples

[ypred,stdypred]=nns_simulbis(wh,wo,x,stdres,covw)

Bibliography

Authors


Report an issue
<< nns_simul (X->y) Non-linear calibrations nns_simulter >>