纺织学报 ›› 2006, Vol. 27 ›› Issue (10): 57-59.

• 研究探讨 • 上一篇    下一篇

BP神经网络预测上浆率

杨艳菲1;崔世忠1;郑天勇1;禹建丽2   

  1. 1.中原工学院纺织学院 河南郑州450007;2.中原工学院研究生处 河南郑州450007
  • 收稿日期:2005-09-13 修回日期:2006-05-22 出版日期:2006-10-15 发布日期:2006-10-15

Predicting size loading with BP neural work

YANG Yan-fei;CUI Shi-zhong;ZHENG Tian-yong;YU Jian-li   

  1. 1.College of Textile;Zhongyuan Institute of Technology;Zhengzhou;Henan 450007;China;2.Postgraduate Office;Zhongyuan Institute of Technology;Zhengzhou;Henan 450007;China
  • Received:2005-09-13 Revised:2006-05-22 Online:2006-10-15 Published:2006-10-15

摘要: 上浆率是衡量浆纱质量的重要指标之一,在生产过程中受到多种工艺因素的影响,其中主要可控因素为浆液浓度、浆槽温度、浆纱机速度和压浆辊压力。为建立以上4个因素与上浆率之间对应关系的数学模型,保证准确预测上浆率,以纯棉精梳斜纹织物实际生产中的经验数据为训练样本,建立3层BP神经网络系统预测模型,采用Levenberg Marquardt算法,对网络进行反复训练,使其达到预设精度。应用该网络模型对上浆率进行预测,结果表明,预测上浆率与实际上浆率非常接近,可以满足实际生产要求。

Abstract: Size loading is one of important indexes in evaluating the sizing quality,which is influenced by several process parameters,including four controllable factors,i.e.,sizing concentration,temperature in sizing box,speed of sizing machine and squeezing roller pressure.To establish a math model of the correlation of above four factors and the size loading for accurate prediction of size loading,a prediction model of three-layer Back-Propagation neural network system was established and the network was trained using the experienced data obtained from the practical production of pure cotton twill and repeated training was carried out with Levenberg Marquardt algorithms so that the performance goal was attained.This network model was applied to predict the size loading and the predicted results showed in good agreement with the actual value,and it can meet the requirements for production.

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