Applications of neural networks for grading textile yarns
The grade of textile yarns is an important index in evaluating the yarns 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.
neural networks k-l expansion dimension reducing feature selection relearning process textile yarn grading system