To train the Self-Organizing Map a set of feature vectors
is extracted from faulty-free samples.
The weight vectors of the map units are initialized to random values evenly
distributed in the area of training vector components.
The training of the map is done by feeding the training vectors to the
map, finding the best-matching unit for each training vector and
updating the weight vectors of the best-matching unit and its neighbors
[9].
The training set is fed into the map several times. As a result we
have a map which has learned an estimate of the distribution of
faulty-free samples.