Fuzzy robust regression analysis

Junzo Watada*, Yoshiyuki Yabuuchi

*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

Since a fuzzy linear regression model has been proposed in 1987, its possibilistic model is employed to analyze data. From view points of fuzzy linear regression, data are understood to express the possibilities of a latent system. When data have error or data are very irregular, the obtained regression model has unnaturally too wide possibility range. In this paper we propose a fuzzy robust linear regression which is not influenced by data with error. The model is built as rigid a model as possible to minimize the total error between the model and the data. The robustness of the proposed model is shown using numerical examples.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages1370-1376
Number of pages7
Volume2
Publication statusPublished - 1994
Externally publishedYes
EventProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA
Duration: 1994 Jun 261994 Jun 29

Other

OtherProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
CityOrlando, FL, USA
Period94/6/2694/6/29

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Fuzzy robust regression analysis'. Together they form a unique fingerprint.

Cite this