Paper Title:
Prediction of Yarn Quality Based on BP Neural Network
  Abstract

As the quality of yarn and the fiber indicators are nonlinear relationship, the traditional mathematical models or empirical formula has been unable to accurately resolve the problem. In view of artificial neural networks do not need to build accurate mathematical models, applicable to solving the problem of yarn quality prediction. In this paper, good nonlinear approximation ability of BP (Back Propagation) neural network be used, the use of neural network toolbox of MATLAB functions for modeling, good results was obtained. Prediction model set a hidden layer, using three-tier network architecture, and take the input layer 4 nodes, hidden layer 8 nodes and output layer 2 nodes. According to forecast results, can ensure the yarn quality effectively, use of raw materials rationally, to achieve optimal distribution of cotton. Meanwhile, the spinning process design can also be provided validation, for the development of new products to provide a theoretical basis.

  Info
Periodical
Chapter
Chapter 3: Textile Machinery and Equipment
Edited by
Rui Wang and Huawu Liu
Pages
449-453
DOI
10.4028/www.scientific.net/AMR.331.449
Citation
J. Yuan, Y. L. Li, S. Y. Chen, "Prediction of Yarn Quality Based on BP Neural Network", Advanced Materials Research, Vol. 331, pp. 449-453, 2011
Online since
September 2011
Export
Price
$35.00
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