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the Fisher test at different values of the confidence levels α%. When this level
increases, the number of significant factors and interactions increased, and thus the
number of inputs of the ANN increases. Average learning
MSE which express the
average error between the training data and the corresponding predicted values and
the generalization error
Eg
which express the deviation from the testing data have
shown the existence of a critical set of inputs offering the highest prediction capability
of the developed ANNs. By using this approach, substantial improvements of the
prediction capability of the ANNs could be realized comparatively with the prediction
ability of the quadratic models developed using the DoE. On the other hand the
developed ANNs have shown better capability comparatively with the commonly
used structures, which use the DoE factors as inputs.
It was also remarked that the developed ANNs present higher sensibility to the input
variations than the DOE as they can distinguish between particular phenomena
occurring at low and high work speeds. At last, problems related to the minimal
negative predicted value, calculated by using models established with DOE method
could be solved as ANNs respects the output sign.
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