日本感性工学会論文誌
Online ISSN : 1884-5258
Print ISSN : 1884-0833
ISSN-L : 1884-5258
原著論文
ネットワークインバージョンを用いたユーザの感性情報に適合するカクテル創出法
奥谷 勝行塩谷 浩之工藤 康生沖井 廣宣
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2010 年 9 巻 2 号 p. 431-437

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Data mining has been used for obtaining a typical structure of given data set in the field of information science, systems engineering and so on. In this paper, the cocktail-data quantification using the distance mapping learning network architecture is introduced as a kind of data mining for Kansei engineering. And moreover, the network inversion method is newly introduced into the cocktail-data quantification in order to obtain a suitable cocktail based on given user's Kansei information. A simple numerical example is presented with the usage of some typical cocktails and their questionnaire.

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© 2010 日本感性工学会
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