Full Text.pdf

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📄 Pages: 20 pages
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Summary

This study investigates a fuzzy linear regression model for crisp/fuzzy input and fuzzy output data using a least absolutes deviations approach, introducing a new metric on the space of fuzzy numbers to handle symmetric and non-symmetric fuzzy observations. The proposed approach is compared to existing models using three goodness of fit criteria and three well-known data sets. The model is ˆYi = a0 ⊕(a1Xi1) ⊕(a2Xi2) ⊕· · · ⊕(apXip) ⊕E, where a0, a1, ..., ap are crisp coefficients, and E is the fuzzy error term. The study reviews existing fuzzy regression models, including those by Tanaka et al., Pourahmad et al., Celmins, and Diamond, and discusses the limitations of previous approaches, such as sensitivity to outlier data points and incorrect distance measurement.

Description

This study investigates a fuzzy linear regression model for crisp/fuzzy input and fuzzy output data using a least absolutes deviations approach, introducing a...

Technical Information

  • File Format: PDF
  • File Size: 415 KB
  • Pages: 20
  • Language: EN
  • Total Downloads: 44
  • Last Updated: 3 weeks ago

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