Regression model based on fuzzy random variables

Shinya Imai, Shuming Wang, Junzo Watada

    研究成果: Conference contribution

    4 被引用数 (Scopus)

    抄録

    In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems. The objective of this paper is to build a regression model based on fuzzy random variables. First, a general regression model for fuzzy random data is proposed. After that, using expected value operators of fuzzy random variables, an expected regression model is established. The expected regression model can be developed by converting the original problem to a task of a linear programming problem. Finally, an explanatory example is provided.

    本文言語English
    ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ページ127-135
    ページ数9
    5179 LNAI
    PART 3
    DOI
    出版ステータスPublished - 2008
    イベント12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb
    継続期間: 2008 9 32008 9 5

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    番号PART 3
    5179 LNAI
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

    Other12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
    CityZagreb
    Period08/9/308/9/5

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)
    • 理論的コンピュータサイエンス

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