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基于模糊关联与BP网络的客户感性知识挖掘
引用本文:石夫乾,孙守迁,徐江.基于模糊关联与BP网络的客户感性知识挖掘[J].工程设计学报,2007,14(5):349-353.
作者姓名:石夫乾  孙守迁  徐江
作者单位:1.温州医学院 计算机系, 浙江 温州 325001; 2.浙江大学 现代工业设计研究所, 浙江 杭州 310027
基金项目:国家自然科学基金 , 高等学校博士学科点专项科研项目
摘    要:通过建立Web问卷调查系统获取用户对产品造型特征的感性反映信息,并对用户感性评价信息予以模糊表征,进行多维模糊关联法则挖掘,进而产生客户感性信息与产品造型特征关联规则高频项目集。利用BP神经网络的学习能力对不同时段关联规则进行训练、预测和整合,从而实现客户感性知识挖掘,为产品设计辅助与企划决策支持提供新思路。

关 键 词:模糊关联法则  倒传递类神经网络  产品设计  
文章编号:1006-754X(2007)05-0349-05
收稿时间:2007-03-22
修稿时间:2007-03-22

Applying fuzzy-association-rule techniques and artificial neural networks to customer Kansei knowledge mining
SHI Fu-qian,SUN Shou-qian,XU Jiang.Applying fuzzy-association-rule techniques and artificial neural networks to customer Kansei knowledge mining[J].Journal of Engineering Design,2007,14(5):349-353.
Authors:SHI Fu-qian  SUN Shou-qian  XU Jiang
Affiliation:1. Computer Department, Wenzhou Medical College, Wenzhou 325000, China ; 2. Modern Industrial Design Institute, Zhejiang University, Hangzhou 310027, China
Abstract:Customers’ Kansei information on product geometry features is obtained through Web survey system. Customers’ Kansei evaluation is in fuzzy expression and then multi-dimension fuzzy-association-rule technology is applied. Andfinally, high-frequency sets in which the geometry features of products are highly associated with customers’ Kansei information are generated. Furthermore, the learning ability of BP neural network are utilized to train, predict and integrate association rules from different time. And then customers’ Kansei knowledge mining is implemented, which provides new ideas for product aid design and enterprise decision-making.
Keywords:fuzzy association rule  back propagation neural network  product design
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