Building a type-2 fuzzy regression model based on credibility theory and its application on arbitrage pricing theory

Yicheng Wei, Junzo Watada

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    Real life circumstances used to provide us with linguistically vague expression of data in nature. Thus, type-1 fuzzy set (T1F set) was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means ambiguous uncertainty also exists when associated with the membership function of a T1F set. Type-2 fuzzy set(T2F set) is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, T2F variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of T2F regression model is built based on credibility theory, called the T2F expected value regression model. The new model will be developed in this paper. This paper is a further work based on our former research of T2F qualitative regression model.

    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2368-2375
    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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