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基于SVM特征选择的整经轴数预测算法 被引量：3

Prediction Algorithm of Trim Beam Number Using Modified SVM-Based Feature Selection
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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. 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.
作者 孙跃鹏 刘民 郝井华 吴澄
机构地区 清华大学自动化系 清华大学国家CIMS工程技术研究中心
出处 《北京邮电大学学报》 EI CAS CSCD 北大核心 2009年第6期88-92,共5页 Journal of Beijing University of Posts and Telecommunications
基金 国家高技术研究发展计划项目(2006AA04Z163) 国家重点基础研究发展计划项目(2002CB312202 2009CB320602) 国家自然科学基金项目(60834004 60721003) 教育部新世纪优秀人才支持计划项目
关键词 支持向量机 整经轴数 特征选择 预测 调度 support vector machine trim beam number feature selection prediction scheduling
分类号 TP273 [自动化与计算机技术—检测技术与自动化装置]
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共引文献58

同被引文献22

  • 1芦涛,王成儒,韩笑蕾.基于SVM的汉语语音情感识别研究[J].电子测量技术,2007,30(3):20-21. 被引量：5
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引证文献3

二级引证文献10

北京邮电大学学报
2009年 第6期

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