This post continues the topic in the following post: Is it possible to train a neural network with 3 inputs and 12 outputs?.
I conducted several experiments in MATLAB and selected those neural networks that best approximate the data.
Here is a list of them:
Cascade-forward backpropagation
Elman backpropagation
Generalized regression
Radial basis (exact fit)
I did not notice a fundamental difference in quality, except for Elman's backpropagation, which had a higher error than the rest.
How to justify the choice of the structure of the neural network in this case?
