Machine-Part Family Formation Utilizing an Art1 Neural Network Implemented on a Parallel Neuro-Computer
Abstract
The ART1 neural network algorithm has been implemented in the past for the classifying and grouping of similar vectors from a machine-part matrix. Recently, a new ART1 paradigm which involves reordering of the input vectors with a modified procedure for storing a group's representation vectors has proven successful in both speed and functionally compared to previous techniques. This new paradigm is now adapted and implemented on a neuro-computer utilising 256 processors, allowing the neural network to take advantage of its inherent parallelism. Tremendous improvements in the speed of the machine-part matrix optimization result from the parallel implementation. Comparisons with the previous serial algorithm are made and suggestions for possible parallel implementation within a manufacturing environment are discussed. © 1998 Published by Elsevier Science Ltd. All rights reserved.
Recommended Citation
D. L. Enke et al., "Machine-Part Family Formation Utilizing an Art1 Neural Network Implemented on a Parallel Neuro-Computer," Computers and Industrial Engineering, vol. 34, no. 1, pp. 189 - 205, Elsevier, Jan 1998.
The definitive version is available at https://doi.org/10.1016/s0360-8352(97)00160-5
Department(s)
Electrical and Computer Engineering
Second Department
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
0360-8352
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 1998
