Using A Synergetic Computer in an Industrial Classification Problem
Synergetic Computers (SCs) 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% under variable illumination conditions and even 100% with constant illumination. In addition to this, we try to specify the SCAP with respect to estabished pattern identification algorithms.
KeywordsRecognition Rate Learning Sample Constant Illumination Hardware Realization Pattern Recognition Task
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