• DocumentCode
    3429152
  • Title

    Prediction of fiber diameter of spunbonding nonwovens by using neural network and multiple regression models

  • Author

    Bo, Zhao

  • Author_Institution
    Coll. of Textiles, Zhongyuan Univ. of Technol., Zhengzhou, China
  • Volume
    2
  • fYear
    2010
  • fDate
    25-27 June 2010
  • Abstract
    In this article, the multiple regression model and neural network model are designed and used to predicting the fiber diameter of spunbonding nonwovens from the process parameters. The neural network has good approximation capability and fast convergence rate, and it can provide quantitative predictions of fiber diameter. The results show the ANN model can yield more accurate and stable predictions than the multiple regression model. The predicted and experimental values agree well, indicating that the neural network is an excellent method for predictors.
  • Keywords
    fabrics; neural nets; prediction theory; production engineering computing; regression analysis; ANN model; convergence rate; fiber diameter prediction; multiple regression model; neural network; process parameter; spunbonding nonwoven; Artificial neural networks; Computer networks; Equations; Neural networks; Neurons; Polymers; Predictive models; Temperature; Textile fibers; Throughput; artificial neural network mode; fiber diameter; multiple regression model; spunbonding nonwoven;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Design and Applications (ICCDA), 2010 International Conference on
  • Conference_Location
    Qinhuangdao
  • Print_ISBN
    978-1-4244-7164-5
  • Electronic_ISBN
    978-1-4244-7164-5
  • Type

    conf

  • DOI
    10.1109/ICCDA.2010.5541361
  • Filename
    5541361