Casestudie Breakdown prediction Contell PILOT - Transumo

V ⊆ N × N is a set of directed connections ( n , n )
{ F : n ∈ N}
is a set of learning functions,
which calculate new weightings
w ( t ) = F ( W ( t ), y(
t ), a(
t ), d)
i
i
2
i
i
1
1
1
i
j
for the neurons :
with
d = aimed output vector ( not necessary in case of
W = weighting matrix
y = output vector
a = activation vector
a selforganized network ( see below)
Formula 5-20: Definition of V and F
Figure 5-6 pictures the above given definition of an artificial neuron. Due to that
functioning, an artificial neural network is similar to a Petri net but dynamic, because
in- and output can vary over time.
Figure 5-6: Functioning of an Artificial Neuron ([Hagen97], p. 8) (adapted)
Just like a human brain, artificial neural networks have to learn. Within this
initialization part, training data is applied to an untrained network to determine the
weightings. These weightings will remain unchanged, if the initialization part is
completed. In most cases, a representative part of the whole available data is taken
as training data. ([Lusti02], p. 320-322)
In general, two approaches of learning do exist:
• Supervised learning
• Unsupervised learning
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