prediction and prediction errors of a neural network with 1 hidden layer and with bias
[ypred,std_ypred]=nns_simulbis(wh,wo,x,std_res,cov_w)
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
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
calibration dataset
x is a matrix of dimensions (n x q) or a Div structure
estimation of the standard deviation of the residuals
std_res is a vector of dimensions (1 x no) or a Div structure
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
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)
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)