• 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