Casestudie Breakdown prediction Contell PILOT - Transumo

The general idea of supervised learning is a feedback of already known results. This
means that during initialization not only inputs are provided but also aimed results.
Hence, the neural network is able to adapt weightings to these aimed results.
Offering these results could be done in two ways. First of all, historical data could be
used that already contains results (e.g. a forecast done by the network can be
evaluated by comparing it to the actually occurred value). The other possibility of
supervised learning is the usage of a trainer. This trainer evaluates the results of
training inputs and rates them. These ratings signalize the network, how weightings
have to be changed. ([Heuer97], p. 16-17)
Hence supervised learning is done by reacting on errors. A common learning
approach is the usage of the delta rule. As described above, the neural network
determines an output vector y to a given input vector x. Moreover, vector d must be
given, which contains the aimed results. To be able to apply the delta rule, the
magnitude of error has to be calculated by using the following Formula 5-21:
([Hagen97], p. 22-23)
δ = d − y
i
i
i
with
δ
i
= Error
d = Aimed result
i
y = Calculated Output
i
i =1,
K,
n
Formula 5-21: Determination of Error
As described above, this error is used to adapt the weightings between the single
neurons. Formula 5-22 contains the often used delta rule that shall exemplify
supervised learning.
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