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Relationships between neural networks, statistical classifiers and synergetic computers

: Schramm, U.; Wagner, T.; Böbel, F.G.; Spinnler, K.P.

Boebel, F.G. ; Fraunhofer-Institut für Integrierte Schaltungen -IIS-, Erlangen:
ICASSE '94. Proceedings of the First International Conference on Applied Synergetic and Synergetic Engineering : June 21 - 23, 1994, Erlangen, Germany
Erlangen: Fraunhofer IIS, 1994
ISBN: 3-8167-4471-0
International Conference on Applied Synergetic and Synergetic Engineering (ICASSE) <1, 1994, Erlangen>
Fraunhofer IIS A ( IIS) ()
industrial quality assurance; industrielle Qualitätssicherung; neural net; neural network; neuronales Netzwerk; synergetic computer; synergetics; synergetischer Computer; Synergie

Neural networks and the recently introduced synergetic computer promise new perspectives in the fields of signal and image processing. The most promising feature of neural networks is their ability to learn from examples. A striking feature of synergetic computers is their mathematical similarity to real physical effects and therefore the possibility of constructing synergetic hardware. The new approaches may be used as classifiers and therefore compete with conventional classifiers e. g. statistical classifiers. What are the differences between the conventional and the new approaches? It is the purpose of this paper to give some answers to that questions.