1. Design multiple open-loop (OL) nets in a double loop over the number of hidden nodes (outer loop) and random weight initializations(inner loop). For examples, search NEWSGROUP and ANSWERS with
greg Hub Ntrials
2. Use divideblock instead of dividerand to preserve correlations
3. Use the validation MSE tr.best_vperf to choose the best design and test MSE tr.best_tperf to estimate performance on unseen data.
4. To use the net on unseen data with only known inputs, convert the OL design to closed-loop (CL).
5. Evaluate the CL net on the design data.
6. If performance is significantly worse than the OL performance, use train to improve the CL performance.
7. Use the CL design with future inputs to predict future outputs.
8. If you know the corresponding future targets, you can evaluate the result.
Hope this helps.