Neither:
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
where
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.,
https://en.wikipedia.org/wiki/Coefficient_of_determination
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
Greg
