Applications of neural networks for grading textile yarns

Hsin Chung Lien, Shyong Lee

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


The grade of textile yarns is an important index in evaluating the yarn's market value. This paper uses the backpropagation neural network (BNN) and Karhunen-Loeve (K-L) expansion method to construct a new and highly accurate grading system. Outcomes show that a highly accurate and neutral grading system can be obtained if the BNN learning sample is comprehensive or by adopting the BNN with a relearning technique (self-healing). Considering the possibility of reducing the dimension of BNN input vectors without losing the accuracy, this paper preprocesses the BNN grading system using the K-L expansion. Experiments demonstrate that the K-L expansion provides a way to reduce the input dimensions, and that a single principle axis value of the BNN with the K-L expansion grading system is able to grade textile yarns. In addition, the experiment demonstrates that as the input dimensions are reduced to four in a self-healing neural network with the K-L expansion, the grading system provides the high accuracy and robustness.

Original languageEnglish
Pages (from-to)185-192
Number of pages8
JournalNeural Computing and Applications
Issue number3
StatePublished - Sep 2004


  • Dimension reducing
  • Feature selection
  • K-L expansion
  • Neural networks
  • Relearning process
  • Textile yarn grading system


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