Casestudie Breakdown prediction Contell PILOT - Transumo

w ( t + 1) = w ( t)
+ α ⋅(
d ( t)
− y ( t)
⋅ x ( t)
= w ( t)
+ α ⋅δ
( t)
⋅ x ( t)
ij
ij
i
i
j
ij
i
j
with
w
α > 0 = Learning rate
δ = Error
x = Given input
d
i
i
i
= Weighting of connection
= Aimed result
y = Calculated Output
i
ij
i = 1, K,
n
from n
j
to n
i
Formula 5-22: The Delta Rule
Unsupervised learning has to be used, if only the question of data analysis but not
the result is available. The general idea is again to train the network with sample
patterns. But this time, the network has to find and evaluate structures itself. A
needed requirement is redundancy within the input vector. The more redundancy, the
better the training results because it allows the identification of noise and
disturbances. ([Heuer97], p. 18; [Hagen97], p. 19)
Most unsupervised approaches use Hebb learning. This principle is adopted from the
human brain because the weighting of the connection between two active neurons
increases. Hebb defined that the weighting is proportional to the product of the two
neurons’ outputs. Formula 5-23 summarizes this approach. ([Hagen97], p. 20)
w ( t + 1) = w ( t)
+ α ⋅ y ( t)
⋅ y ( t)
ij
ij
i
j
with
w
= Weighting of connection
α > 0 = Learning rate
y = Calculated Output
i
ij
from n
j
to n
i
Formula 5-23: Hebb Learning Rule
5.9.3 Non-Applicability of Artificial Neural Networks to Current Datasets
Although many different specific artificial neural networks do exist, they are always
based on either supervised or unsupervised learning methods. An application of an
unsupervised artificial neural network to currently obtained temperature data is not
possible, because each input vector would only contain a time, a temperature and
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