Optimal hidden nodes number

 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% of the training subset target variance is
    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.

greg fitnet/patternnet msegoal nmse