Fuzzy robust regression analysis based on a hyperelliptic function

Junzo Watada*, Yoshiyuki Yabuuchi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


Since a fuzzy linear regression model was proposed in 1987, its possibilistic model is employed to analyze data in various fields. From view points of fuzzy linear regression, data are interpreted to express the possibilities of a latent system. Therefore, when data have error or samples are irregular, the obtained regression model has unnaturally too wide possibility range. In this paper we propose a fuzzy robust linear regression model which is not influenced by data with error. Especially a hyperelliptic function is employed to select focal samples which may have large error or be irregular so that the number of combinatorial calculations can be reduced to a great extent. The model is built to minimize the total error between the model and the data. The robustness of the model is shown using numerical examples.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Editors Anon
Place of PublicationPiscataway, NJ, United States
Number of pages8
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5) - Yokohama, Jpn
Duration: 1995 Mar 201995 Mar 24


OtherProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5)
CityYokohama, Jpn

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality


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