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Using a synergetic computer in an industrial classification problem


Albrecht, R.F.; Reeves, C.R.; Steele, N.C.:
Artificial neural nets and genetic algorithms. Proceedings of the International Conference 1993
Wien: Springer, 1993
ISBN: 0-387-82459-6
ISBN: 3-211-82459-6
International Conference on Artificial Neural Nets and Genetic Algorithms (ANNGA) <1, 1993, Innsbruck>
Conference Paper
Fraunhofer IIS A ( IIS) ()
algorithm; Algorithmus; Mustererkennung; pattern recognition; synergetic computer; synergetics; synergetischer Computer; Synergie

Synergetic Computers (SC) represent a class of new algorithms which can be used for different pattern recognition tasks. Due to their strong mathematical similarity with self-organized phenomena of physical nature they embody promising candidates for hardware realizations of classification systems. Until now, there is still a lack of investigations concerning the importance of synergetic algorithms in the field of pattern recognition as well as concerning their practical performance. One of these synergetic algorithms (SCAP) will be examined in this paper with respect to pattern recognition capabilities. Its capacity of identifying wheels in an industrial environment is discussed. We show that with adequate preprocessing the SCAP reaches recognition rates of 99,3 per cent under variable illumination conditions and even 100 per cent with constant illumination. In addition to this, we try to specify the SCAP with respect to established pattern identification algorithms.