In this paper, we propose an automatic Kansei fuzzy rule creating system using thesaurus. In general, there are a lot of words that express impressions. However, conventional approaches of Kansei engineering are not suitable to use many impression words because it is difficult to collect enough data. The proposed system is an enhanced algorithm of the conventional method that the authors proposed before. The proposed system extracts fuzzy rules for many words defined in the thesaurus dictionary while the conventional one can extract rules of specified words which user defined. The flow of the system consists of 3 steps: (1) construction of thesaurus networks; (2) data collection by web questionnaire sheets; (3) Extraction of fuzzy rules. In order to extract Kansei fuzzy rules, the system employs enhanced GRNN(general regression neural network) which can treat relative words of the thesaurus network. Using a Japanese thesaurus dictionary in the experiments, the sets of fuzzy rules for 1,195 impression words are extracted, and the fuzzy rules extracted by the proposed system obtained higher accuracy than those extracted by the conventional one.
|ジャーナル||Transactions of the Japanese Society for Artificial Intelligence|
|出版ステータス||Published - 2008|
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