Is there a rule/good advice on how big a artificial neural network should be?

Question

My last lecture on ANN's was a while ago but I'm currently facing a project where I would want to use one.

So, the basics - like what type (a mutli-layer feedforward network), trained by an evolutionary algorithm (thats a given by the project), how many input-neurons (8) and how many ouput-neurons (7) - are set. But I'm currently trying to figure out how many hidden layers I should use and how many neurons in each of these layers (the ea doesn't modify the network itself, but only the weights).

Is there a general rule or maybe a guideline on how to figure this out?

Answer 1

The best approach for this problem is to implement the cascade correlation algorithm, in which hidden nodes are sequentially added as necessary to reduce the error rate of the network. This has been demonstrated to be very useful in practice.

An alternative, of course, is a brute-force test of various values. I don't think simple answers such as "10 or 20 is good" are meaningful because you are directly addressing the separability of the data in high-dimensional space by the basis function.