纺织学报 ›› 2008, Vol. 29 ›› Issue (9): 34-37.

• 纺织工程 • 上一篇    下一篇

主成分分析的BP神经网络在精毛纺粗纱工序中的应用

刘贵1;于伟东2.3   

  1. 1.东华大学纺织材料与技术实验室 上海 201620;2.嘉兴学院 浙江嘉兴 314001;3.东北大学 纺织学院 上海201620
  • 收稿日期:2007-09-18 修回日期:2008-04-02 出版日期:2008-09-15 发布日期:2008-09-15

Application of principal component analysis and BP neural network in the worsted roving spinning

LIU Gui;YU Weidong   

  1. 1.Textile Materials and Technology Laboratory;Donghua University;Shanghai 201620;China;2.College of Garment and Art Design;Jiaxing University;Jiaxing;Zhejiang 314001;China;3.Cellege of Textiles;Donghua University;Shanghai\ 201620;China
  • Received:2007-09-18 Revised:2008-04-02 Online:2008-09-15 Published:2008-09-15

摘要: 1.Textile Materials and Technology Laboratory;Donghua University;Shanghai 201620;China;2.College of Garment and Art Design;Jiaxing University;Jiaxing;Zhejiang 314001;China;3.Cellege of Textiles;Donghua University;Shanghai\ 201620;China

Abstract: The characteristics of worsted roving procedure and BP neural network have been analyzed.Based on interdependence of all parameters,the principal component analysis(PCA) has been proposed to preprocess the roving procedure′s data collected from a worsted mill.The five general indexes: fiber characteristic,top weight unevenness,top drafting state,top impurities content and top drawing state have been gained,thus eliminating the original variables′interdependence.After PCA the data were inputted to BP network for modeling.The results indicate that the number of node decreased;the network structure simplified;the performance and learning rate of network enhanced.The relative mean error percent between the forecast values ofCVand weight of 20 groups of test samples of roving and the measured values are 2.24% and 1.95% compared to 4.86% and 3.35% before PCA respectively;the precision is improved.The correlation coefficient between the predict value and measured value is also distinctly enhanced.

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