1. ALWAYS START WITH 10 DESIGNS USING THE MATLAB DEFAULT!
2. Then evaluate the results to determine what to modify.
3. For regression the default is FITNET. So, look at the codes in
4. They are the same:
[ x, t ] = simplefit_dataset;
net = fitnet(H); % H = 10 hidden nodes
net = train(net,x,t);
perf = perform(net,t,y)
5. Since I don't trust "perform" , I add a normalized mean square error calculation which typically has a range from 0 to 1
NMSE = mse(t-y)/mse(t-mean(t)) % 0 <= NMSE <= 1
7. Search using
8. This is related to the familiar Rsquare (coefficient of determination) used in elementary statistics
(See any encyclopedia)
Rsquare = 1-NMSE
9. If successful, the next step is to try to obtain good results with the number of hidden nodes
H < 10
10. Otherwise, increase H.
11. I have a jillion examples in both the NEWSGROUP and ANSWERS.
PS: This format sucks.