If you want to rank input importance, looking at the input weights is not sufficient.
1. Standardize input and target variables (zero-mean/unit-variance)
2. Train the net using all of the variables
3. The simplest way to obtain input rankings is to rank the individual increases in MSE when each input variable row is replaced by the zero variance variable row with all values equal to the mean value of the original row.
replx(i,:) = repmat(mean(x(i,:),2), 1, N);
4. The next simplest way is to continue training the original net after the constant row replacement before measuring the increase in MSE.
5. A more convincing method is to replace each input variable row with a random permutation of it's values. This can be repeated Ntrial (e.g., Ntrials = 20) times for each row.
replx(i,:) = repmat(x(i,randperm(1:N)));
6. Same as 5 except that training is continued after each random permutation.
7. Each ranking procedure can be extended to a sequential reduction of variables by discarding the lowest ranked input after each step.
Other toolboxes contain nonneural statistical methods for ranking and reduction variables. However, using a neural approach for a neural model usually yields the best results.
Hope this helps.
Greg
