Fuzzy Mathematical Methods for Kansei Information Processing
| Project/Area Number |
09838038
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
感性工学
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| Research Institution | Japan Advanced Institute of Science and Technology, Hokuriku (1998-1999)
Konan University (1997) |
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Principal Investigator |
NAKAMORI Yoshiteru Japan Advanced Institute of Science and Technology, School of Knowledge Science, Professor, 知識科学研究科, 教授 (30148598)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,700,000 (Direct Cost: ¥2,700,000)
Fiscal Year 1999: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1998: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1997: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Kansei Engineering / Kansei Information / Fuzzy Data Analysis / Fuzzy Integral / Fuzzy Reasoning / 感性情報処理 / ファジィ因子分析 / ファジィ数量化理論 |
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| Research Abstract |
Kansei Engineering and Kansei Information Processing are often used recently to understand the image or feeling of people for physical objects. A set of qualitative data obtained by rating a product usually has a large variance reflecting tastes and preferences of individuals. It is sensible to express such fluctuations by fuzzy numbers to treat vagueness and uncertainty of the feeling of individuals. This paper proposes a factor analysis technique for fuzzy data of rating scores measured by words that are mainly adjectives such as innovative, bright, elegant or cheerful.
Fuzzy factor loading is determined as fuzzy numbers through a data mapping technique. Then, words are identified as fuzzy objects in the factor space. After fuzzy distances between words in the factor space are defined, a covering technique is used to determine a set of representative words and a partition of words simultaneously. This provides a useful information to study the relation between words and design elements. Then, a fuzzy quantification method is developed based on the fuzzy regression analysis. Finally, a fuzzy reasoning method from words to designs is proposed. A concrete example is used throughout the paper to show the effectiveness of proposed techniques.
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Report
(4 results)
Research Products
(15 results)
