1. The default performance function of the regression NNs NEWFIT (calls the generic NEWFF; both are obsolete but still available) and FITNET (current: calls the generic FEEDFORWARDNET) is mean-square-error, MSE, which is scale dependent.
2. However, it is better to use the SCALE INDEPENDENT NORMALIZED MSE, NMSE. MSE is normalized by the MSE of the simplest NN model: the one whose output is just a constant, INDEPENDENT OF THE INPUT! In order to minimize that MSE, the constant must be the average target variance.
In recent threads I have used the notation vart1. In earlier threads I have used the notation MSE00:
NMSE = MSE/MSE00 = MSE/vart1
MSE00 = vart1 = mean(var(target',1))
3. This is not a frivolous choice: NMSE is the fraction of the average target variance that is NOT modelled by the net. Conversely, the "Coefficient of Variation" also known as "R-squared" defined by
is the fraction of the average target variance that IS modelled by the net!
Lookup RSQUARE in both GOOGLE and WIKIPEDIA, e.g.,
My typical choice of the regression design goal is
which yields Rsq = 0.99 (Rsq = 1 is a perfect fit!).
There are hundreds of my examples in both the NEWSGROUP and ANSWERS.
Hope this helps.
Thank you for formally accepting my answer