prediction errors by a neural network with 1 hidden layer and with bias
[sse,gra,hes]=nns_simulter(wh,wo,x,y)
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
reference values, to be predicted
y is a matrix of dimensions (n x no) or a Div structure
sum of squares of the errors, for each output
sse is a Div structure
sse.d is a vector of dimensions (1 x no)
partial derivatives of the sums of square of the errors, with respect to each weight, for each output
gra is a Div structure
gra.d is a matrix of dimensions (nw x no)
nw=(q+1)*nh + (nh+1)*no
Hessians, approximate second order derivatives of the sums of squares of the errors with respect to the weights, for each output
hes is an hypermatrix of dimensions (nw x nw x no) or a Div structure