This post continues the topic:

Neural network with 3 input 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 backprop;

Elman backprop;

Generalized regression;

Radial basis (exact fit);

I did not notice a fundamental difference in quality, unless Elman's backprop had a higher error than the rest.

How to justify the choice of the structure of the neural network in this case?

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?