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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?

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