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Tutorial
ANFIS training completed at epoch 200.
Minimal training RMSE = 0.326566
Minimal checking RMSE = 0.582545
This looks like good news. The error with the training data is the lowest we’ve
seen, and the error with the checking data is also lower than before, though not
by much at all. This suggests that maybe we had gotten about as close as
possible with this system already. Maybe we have even gone so far as to overfit
the system to the training data. Overfitting occurs when we fit the fuzzy
system to the training data so well that it no longer does a very good job of
fitting the checking data. The result is a loss of generality. A look at the error
history against both the training data and the checking data reveals much.
0.7
0.65
0.6
0.55
Training Error
Checking Error
0.5
0.45
0.4
0.35
0.3
0
20
40
60
80
100
120
140
160
180
200
This is indeed a case of overfitting. The smallest error against the checking
data occurs at epoch 52 after which the checking data error trends upward even
as ANFIS keeps working to minimize the error against the training data all the
way to epoch 200.
References
The fuzzy c-means algorithm is described in [Bez81], while a full description of
the subclust algorithm and the underlying clustering method can be found in
the paper by Chiu [Chi94]. Both are listed in the “References” section at the end
of this chapter.
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