...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Production Research >A yield forecast model for pilot products using support vector regression and manufacturing experience - the case of large-size polariser
【24h】

A yield forecast model for pilot products using support vector regression and manufacturing experience - the case of large-size polariser

机译：利用支持向量回归和制造经验的中试产品产量预测模型-以大型偏光片为例

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To build up a manufacturing management model for a newly developed product is fundamentally a difficult problem, because the collected data in the early manufacturing stages is usually insufficient when data size is small. There are several researches on this topic, and most of them focus on the original data analysis such as building up virtual samples to increase the data number. As to other approaches, the usage of old or similar manufacturing experience may be an alternative approach to help in modelling a small data set, by taking advantage of the fact that the new product's manufacturing process could be based on the experience of the old one. This research proposes a combination of support vector regression (SVR) and the manufacturing experience to build up the manufacturing knowledge model for a new product. A real-problem of a new product yield forecast model in a polariser manufacturing company is demonstrated, where two approaches are proposed, and the results show that the presented approach is superior to the performance of a linear regression and back-propagation neural network. The case study shows that the input of the old or similar manufacturing experience into the forecast model can reduce the error rate and enhance the model forecasting ability.
机译：从根本上来说，为新开发的产品建立制造管理模型是一个困难的问题，因为当数据量较小时，早期制造阶段收集的数据通常不足。关于此主题的研究很多，其中大多数集中在原始数据分析上，例如建立虚拟样本以增加数据数量。关于其他方法，通过利用新产品的制造过程可以基于旧产品的经验这一事实，可以使用旧的或类似的制造经验来帮助建模小型数据集。这项研究提出了支持向量回归（SVR）和制造经验的结合，以建立新产品的制造知识模型。演示了偏振器制造公司中新产品产量预测模型的一个实际问题，其中提出了两种方法，结果表明，该方法优于线性回归和反向传播神经网络的性能。案例研究表明，将旧的或类似的制造经验输入到预测模型中可以减少错误率并增强模型的预测能力。

著录项

  • 来源
    《International Journal of Production Research》 |2010年第18期|P.5481-5496|共16页
  • 作者

    Der-Chiang Li; Chiao-Wen Liu; Yao-Hwei Fang; Cheng-Chien Chen;

  • 作者单位

    Department of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan, Taiwan 70101, ROC;

    rnDepartment of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan, Taiwan 70101, ROC;

    rnDepartment of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan, Taiwan 70101, ROC;

    rnDepartment of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan, Taiwan 70101, ROC;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    polariser; yield forecast; linear regression; SVR; small data set;

    机译：偏光片产量预测;线性回归SVR;小数据集;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号