caa a22 4500 469072792 CHVBK 20180323132926.0 cr unu---uuuuu 170328e19920601xx s 000 0 eng 10.1007/BF01580145 doi (NATIONALLICENCE)springer-10.1007/BF01580145 Peters Thomas Dept. of Computer Science and Engineering, University of Connecticut, 06269-3155, Storrs, CT, USA aut Encoding mechanical design features for recognition via neural nets [Elektronische Daten] [Thomas Peters] Within mechanical computer-aided design (CAD), pattern-recognition techniques are fundamental to feature recognition. The use of neural net software as the pattern-recognition element depends upon encoding schemes which extract critical information from candidate geometric subsets. The trained memory can then determine if a particular candidate geometric subset corresponds to a feature of interest. Successful experiments with particular encoding schemes over a restricted class of features will be presented. Neural nets were chosen with the long-term view toward a feature-recognition architecture where the end-user could customize the domain of features that can be recognized. The training of the neural net memory would be achieved through a graceful graphics interface. Extensive programming and knowledge bases would be avoided. This envisioned architecture will be presented to provide a context for the encoding schemes. Springer-Verlag New York Inc, 1992 Research in Engineering Design Springer-Verlag 4/2(1992-06-01), 67-74 0934-9839 4:2<67 1992 4 163 https://doi.org/10.1007/BF01580145 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF01580145 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Peters Thomas Dept. of Computer Science and Engineering, University of Connecticut, 06269-3155, Storrs, CT, USA aut NATIONALLICENCE 773 0- Research in Engineering Design Springer-Verlag 4/2(1992-06-01), 67-74 0934-9839 4:2<67 1992 4 163 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer