纺织学报 ›› 2011, Vol. 32 ›› Issue (7): 60-64.

• 纺织工程 • 上一篇    下一篇

基于最小方差支持向量机的织物热湿舒适性预测

辛芳芳   

  1. 上海工程技术大学服装学院
  • 收稿日期:2010-07-12 修回日期:2010-12-20 出版日期:2011-07-15 发布日期:2011-09-15
  • 通讯作者: 辛芳芳 E-mail:xin_fangfang1972@126.com
  • 基金资助:

    市级

Prediction of Fabric Thermal-wet Comfort Properties based on Least Squares Support Vector Machines

  • Received:2010-07-12 Revised:2010-12-20 Online:2011-07-15 Published:2011-09-15

摘要: 本文分析了36种针织面料的热湿舒适性客观指标与人体穿着对织物的热湿舒适性主观评定之间的对应关系,并利用最小方差支持向量机(LSSVM)建立了客观指标与主观评定之间的回归模型。本文利用统计学的方法对回归模型进行了评估,并与BP神经网络模型进行了比较,分析结果证明,LSSVM回归模型比BP神经网络模型能够更加准确地预测织物的主观热湿舒适感。

Abstract: We investigate 36 kinds of knitted fabrics and their thermal-wet comfort objective evaluation indices and their subjective in-wear evaluation indices. Based on the least squares support vector machines (LSSVM), we create regression models to predict the subjective evaluation using objective evaluation indices as input. We systematically evaluate the learned regression model using statistical learning methods. Moreover, we compare the regression model based on LSSVM with the regression model based on the back propagation neural network (BP). According to the experimental results, the LSSVM model yields more accurate predictions on subjective thermal-wet evaluations than the BP model.

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