DocumentCode :
2741107
Title :
An industrial application to neural networks to reusable design
Author :
Caudell, T.P. ; Johnson, G.C. ; Wunsch, D.C. ; Escobedo, R.
Author_Institution :
Boeing Comput. Services, Seattle, WA
fYear :
1991
fDate :
8-14 Jul 1991
Abstract :
Summary form only given, as follows. The feasibility of training an adaptive resonance theory (ART-1) network to first cluster aircraft parts into families, and then to recall the most similar family when presented a new part has been demonstrated, ART-1 networks were used to adaptively group similar input vectors. The inputs to the network were generated directly from computer-aided designs of the parts and consist of binary vectors which represent bit maps of the features of the parts. This application, referred to as group technology, is of large practical value to industry, making it possible to avoid duplication of design efforts
Keywords :
CAD; adaptive systems; aerospace computing; aircraft; learning systems; neural nets; resonance; vectors; ART-1 networks; adaptive resonance theory; aircraft parts; aviation industry; binary vectors; bit maps; clustering; computer-aided designs; group technology; input vectors; neural networks; reusable design; training; Aerospace engineering; Aircraft propulsion; Application software; Computer industry; Computer networks; Design automation; Design engineering; Industrial training; Neural networks; Resonance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
Type :
conf
DOI :
10.1109/IJCNN.1991.155571
Filename :
155571
Link To Document :
بازگشت