Building a type-2 fuzzy qualitative regression model

Yicheng Wei, Junzo Watada

    Research output: Contribution to journalArticle

    8 Citations (Scopus)

    Abstract

    Type-1 fuzzy regression model is constructed with type-1 fuzzy coefficients dealing with real value inputs and outputs. From the fuzzy set-theoretical point of view, uncertainty also exists when associated with qualitative data (membership degrees). This paper intends to build a qualitative regression model to measure uncertainty by applying the type-2 fuzzy set as the model's coefficients. We are thus able to quantitatively describe the relationship between qualitative object variables and qualitative values of multivariate attributes (membership degree or type-1 fuzzy set), which are given by subjective recognition and judgment. We will build a basic qualitative model first and then improve it capable of ranging inputs. We will also give a heuristic solution in the end.

    Original languageEnglish
    Pages (from-to)527-532
    Number of pages6
    JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
    Volume16
    Issue number4
    Publication statusPublished - 2012 Jun

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    Fuzzy sets
    Uncertainty

    Keywords

    • Linear programming
    • Quantification
    • Type-1 fuzzy set
    • Type-2 fuzzy qualitative regression model
    • Type-2 fuzzy set

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction

    Cite this

    Building a type-2 fuzzy qualitative regression model. / Wei, Yicheng; Watada, Junzo.

    In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 16, No. 4, 06.2012, p. 527-532.

    Research output: Contribution to journalArticle

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