Optimal hidden nodes number

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Optimal hidden nodes number

Hello everybody,

In order to determine optimal hidden neurons, Trial and error algorithm has been used (trial = 10, 10 < H < 100, dH = 100). I get the table on top but i can not determine the optimal hidden neurons. The table contains (Trials, Hidden neurons, test_mse, train_mse, val_mse, test_R, train_R, val_R)


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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% 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.

4. Suggested NEWSGROUP and ANSWERS search words for either FITNET or PATTERNNET



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