C++ Neural Networks and Fuzzy Logic: Preface

3.

Eliminate correlated inputs. You may optionally try at this point to see if you can eliminate

correlated inputs, as mentioned before, by iteratively removing each input and noting the best error

you can achieve on the training and test sets for each of these cases. Choose the case that leads to the

best error and eliminate the input (if any) that achieved it. You can repeat this whole process again to

try to eliminate another input variable.

4.

Iteratively train and test. Now you can try other network parameters and repeat the train and

test process to achieve a better result.

5.

Deploy your network. You now can use the blind test data set to see how your optimized

network performs. If the error is not satisfactory, then you need to re−enter the design phase or the

train and test phase.

6.

Revisit your network design when conditions change. You need to retrain your network when

you have reason to think that you have new information relevant to the problem you are modeling. If

you have a neural network that tries to predict the weekly change in the S&P 500, then you likely will

need to retrain your network at least once a month, if not once a week. If you find that the network no

longer generalizes well with the new information, you need to re−enter the design phase.

If this sounds like a lot of work, it is! Now, let’s try our luck at forecasting by going through a subset of the

steps outlined: