Applications of neural networks for grading textile yarns

Authors:
Hsin-Chung Lien;Shyong Lee
Affiliations:
Kuang Wu Institute of Technology, Department of Mechanical Engineering, 151 I-De St. Peitou, Taipei, Taiwan 112, R.O.C.;National Central University. No. 38, Department of Mechanical Engineering, Chung-li, Wu-chuan Li, Tao-yuan, Taiwan 320, R.O.C.
Venue:
Neural Computing and Applications
Year:
2004

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Abstract

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.