I have posted hundreds of examples in both the NEWSGROUP (comp.soft-sys.matlab) and ANSWERS that determine the optimal number of hidden nodes defined by
1. One Hidden Layer (ALWAYS SUFFICIENT!!!)
2. Minimum Number of Hidden Nodes subject to my
practicality constraint
TRAINING SUBSET RSQUARE >= 0.99
i.e.
99
successfully modeled by the net.
Equivalently
TRAINING SUBSET MSE <= 0.01*TRAINING SUBSET VARIANCE
3. COMMENTS & CAVEATS
a. The training subset must be a good representative of
validation and test data
b. A smaller number of hidden nodes can often be obtained
by using multiple hidden layers
c. The MSE minimization technique used for regression and
curvefitting (e.g., via FITNET)is also successful for classification
and pattern recognition (e.g., via PATTERNNET) where the
minimization function is cross-entropy and the desired result is
minimal error rate.
4. Suggested NEWSGROUP and ANSWERS search words for either FITNET or PATTERNNET
greg fitnet/patternnet msegoal nmse
5. The method is also used for timeseries
Hope this helps.
Thank you for formally accepting this answer
Greg
Best Answer