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.
D. C. Wunsch et al., "An Industrial Application to Neural Networks to Reusable Design," Proceedings of the International Joint Conference on Neural Networks, 1991., IJCNN-91-Seattle, Institute of Electrical and Electronics Engineers (IEEE), Jan 1991.
The definitive version is available at https://doi.org/10.1109/IJCNN.1991.155571
International Joint Conference on Neural Networks, 1991., IJCNN-91-Seattle
Electrical and Computer Engineering
Keywords and Phrases
ART-1 Networks; CAD; Adaptive Resonance Theory; Adaptive Systems; Aerospace Computing; Aircraft; Aircraft Parts; Aviation Industry; Binary Vectors; Bit Maps; Clustering; Computer-Aided Designs; Group Technology; Input Vectors; Learning Systems; Neural Nets; Neural Networks; Resonance; Reusable Design; Training; Vectors
Article - Conference proceedings
© 1991 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.