Building linguistic random regression model from the perspective of type-2 fuzzy set

Fei Song, Shinya Imai, Junzo Watada

    研究成果: Conference contribution

    抄録

    Information given in linguistic terms around real life sometimes is vague in meaning, as type-1 fuzzy set was introduced to modulate this uncertainty. Meanwhile, same word may result in various meaning to people, indicating the uncertainty also exist when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set attempt to express the hybrid uncertainty of both primary and secondary fuzziness, in order to address regression problems, we built a type-2 Linguistic Random Regression Model based on credibility theory. Confidence intervals are constructed for fuzzy input and output, and the proposed regression model give a rise to a nonlinear programming problem focus on a well-trained model, which would be helpful and useful in linguistic assessment cases. Finally, a numerical example is provided.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Fuzzy Systems
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ2376-2383
    ページ数8
    ISBN(印刷版)9781479920723
    DOI
    出版ステータスPublished - 2014 9 4
    イベント2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing
    継続期間: 2014 7 62014 7 11

    Other

    Other2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
    CityBeijing
    Period14/7/614/7/11

    ASJC Scopus subject areas

    • ソフトウェア
    • 人工知能
    • 応用数学
    • 理論的コンピュータサイエンス

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