For now let's assume one hidden layer with 10 neurons 1:2 delay NARNET.
How to calculate AIC and BIC values?
So far i found that one way is suggested by warren-sarle
AIC = (n)log(SSE/n)+2p
BIC = (n)log(SSE/n)+(p)log(n)
Where: SSE be the sum of squared errors for the training set, n be the number of training cases, p be the number of parameters (weights and biases).
What's training cases and how to calculate them? Is there any function to get number of neural network parameters (like for example vgxcount for VARX models)?
Other option could be:
[aic,bic] = aicbic(logL, numParam, numObs);
I don't know if this suggestion is suitable, but there is clear problem with assumption that numParam is same as number of outputs, but then again, how to get number of neural network parameters?
Is the solution for AIC and BIC calculation same for NARX, fitnet and other neural network models?