Formulation of fuzzy random regression model

Junzo Watada, Shuming Wang, Witold Pedrycz

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables. To address regression problems in presence of such hybrid uncertain data, fuzzy random variables are introduced in this study, and serve as an integral component of regression models. A new class of fuzzy regression models based on fuzzy random data is built, and is called the fuzzy random regression model (FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, σ-confidence intervals are constructed for fuzzy random input-output data. The FRRM is established based on the σ-confidence intervals. The proposed regression model gives rise to a non-linear programming problem which consists of fuzzy numbers or interval numbers. Since sign-changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic non-linearity of this optimization makes it hard to exploit the techniques of linear programming or classical non-linear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.

    Original languageEnglish
    Title of host publicationStudies in Computational Intelligence
    Pages1-20
    Number of pages20
    Volume372
    DOIs
    Publication statusPublished - 2011

    Publication series

    NameStudies in Computational Intelligence
    Volume372
    ISSN (Print)1860949X

    Fingerprint

    Random variables
    Nonlinear programming
    Regression analysis
    Linear programming

    Keywords

    • confidence interval
    • expected value
    • Fuzzy random regression model
    • fuzzy random variable
    • Fuzzy regression model
    • variance

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Watada, J., Wang, S., & Pedrycz, W. (2011). Formulation of fuzzy random regression model. In Studies in Computational Intelligence (Vol. 372, pp. 1-20). (Studies in Computational Intelligence; Vol. 372). https://doi.org/10.1007/978-3-642-11739-8_1

    Formulation of fuzzy random regression model. / Watada, Junzo; Wang, Shuming; Pedrycz, Witold.

    Studies in Computational Intelligence. Vol. 372 2011. p. 1-20 (Studies in Computational Intelligence; Vol. 372).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Watada, J, Wang, S & Pedrycz, W 2011, Formulation of fuzzy random regression model. in Studies in Computational Intelligence. vol. 372, Studies in Computational Intelligence, vol. 372, pp. 1-20. https://doi.org/10.1007/978-3-642-11739-8_1
    Watada J, Wang S, Pedrycz W. Formulation of fuzzy random regression model. In Studies in Computational Intelligence. Vol. 372. 2011. p. 1-20. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-642-11739-8_1
    Watada, Junzo ; Wang, Shuming ; Pedrycz, Witold. / Formulation of fuzzy random regression model. Studies in Computational Intelligence. Vol. 372 2011. pp. 1-20 (Studies in Computational Intelligence).
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