北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2009, Vol. 32 ›› Issue (6): 88-92.doi: 10.13190/jbupt.200906.88.sunyp

• 论文 • 上一篇    下一篇

基于SVM特征选择的整经轴数预测算法

孙跃鹏;刘民;郝井华;吴澄   

  1. (1. 清华大学 自动化系, 北京 100084; 2. 清华大学 国家CIMS工程技术
    研究中心, 北京 100084)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-12-28 发布日期:2009-12-28
  • 通讯作者: 孙跃鹏

Prediction Algorithm of Trim Beam Number Using Modified SVM-Based Feature Selection

SUN Yue-peng;LIU Min;HAO Jing-hua;WU Cheng   

  1. (1. Department of Automation, Tsinghua University, Beijing 100084, Chin
    a;2. National CIMS Engineering Research Center, Tsinghua University, Beijing 100084, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-12-28 Published:2009-12-28
  • Contact: SUN Yue-peng

摘要:

提出了一种基于改进支持向量机(SVM)特征选择算法及神经网络的整经轴数预测算法,该算法采用改进SVM算法选择影响整经轴数的关键特征,在此基础上利用前馈神经网络获得整经轴数的预测值. 在数值计算及实际制造企业的应用效果表明该算法有效,能满足实际棉纺生产过程整经轴数预测的需要.

关键词: 支持向量机, 整经轴数, 特征选择, 预测, 调度

Abstract:

The trim beam number is an important parameter in the scheduling model of the cotton spinning manufacturing process. Because of the complexity of the trim technique, the actual trim beam number is difficult to obtain before scheduling. A prediction algorithm using a modified support vector machine (SVM)-based feature selection method and feed forward neural network (FFNN) is presented for predicting the trim beam number. In the algorithm, the proposed feature selection method is adopted to pick up critical features that affect the trim beam number, and FFNN is adopted to predict the trim beam number based on the critical features. Numerical computational results show that the proposed algorithm is effective. The algorithm also successfully applies in the related problems in practical cotton textile manufacturing system.

Key words: support vector machine, trim beam number, feature selection, prediction, scheduling