纺织学报 ›› 2010, Vol. 31 ›› Issue (5): 141-145.

• 管理与信息化 • 上一篇    下一篇

面向大批量定制的基于改进的LS-SVM服装需求预测模型

张秀美1;孙永剑2;郭亮伟3   

  1. 1. 杭州万向职业技术学院经济管理学院2. 浙江理工大学先进制造研究所3. 宁波雅戈尔集团股份有限公司
  • 收稿日期:2009-03-23 修回日期:2010-01-11 出版日期:2010-05-15 发布日期:2010-05-15
  • 通讯作者: 孙永剑

Forecasting model for apparel demand based on improved least squares support vector machine (LS-SVM)oriented to mass customization

ZHANG Xiumei1; SUN Yongjian2; GUO Liangwei3

  

  1. 1. Department of Economics and Management, Hangzhou Wanxiang Polytechnic College 2.Institute of Advanced Mechanical Technology, Zhejiang Sci-Tech University 3.Youngor Group Co., Ltd
  • Received:2009-03-23 Revised:2010-01-11 Online:2010-05-15 Published:2010-05-15
  • Contact: SUN Yongjian

摘要:

为提高服装需求预测精度,充分考虑了服装需求量随季节、气候条件、价格、性别等因素的影响而动态变化的情况,运用模糊理论对相关影响因素进行模糊化处理后,再将这些影响因素作为服装需求量预测函数的输入变量;然后建立了以改进的二乘支持向量机(LS-SVM)方法为主、多方法融合为辅的预测模型,对服装销售量进行动态预测。实际算例验证了这一智能预测模型具有良好的精确性。

Abstract:

For improving forecast accuracy of apparel demand, this paper, having given full its consideration of affecting factors such as season, climate conditions, price, gender etc. developed a forecast model mainly based on least squares support vector machine, including processing the above factors with fuzzy theory and using these factors as input variables. A forecasting model mainly based on improved least square support vector machine (LS-SVM) and other methods was constructed. Dynamic forecast of apparel demand is achieved, and practical applications show that this intelligent forecasting model has high accuracy.

中图分类号: 

  • TP14
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!