Fuzzy regression analysis by entropy

Chiang Kao, Pei Huang Lin

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

1 Citation (Scopus)

Abstract

To construct a regression model for fuzzy numbers, this paper decomposes a fuzzy number into two parts: the position and fuzziness. The former is represented by the elements with membership value 1 and the latter by the entropy of the fuzzy number, both have crisp values. The conventional regression analysis is applied to find the relationship between the position (and entropy) of the fuzzy response variable and that of the fuzzy explanatory variables. Given a set of fuzzy explanatory variables, the position and entropy of the estimated fuzzy responses are calculated from the regression model. Via the one-to-one correspondence between a fuzzy number and its entropy, the estimated fuzzy response is obtained.

Original languageEnglish
Title of host publication2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings
EditorsR.R. Yager, V.S. Sgurev, V.S. Jotsov, P.D. Koprinkova-Hristova
Pages231-236
Number of pages6
Publication statusPublished - 2004 Dec 1
Event2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings - Varna, Bulgaria
Duration: 2004 Jun 222004 Jun 24

Publication series

Name2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings
Volume1

Other

Other2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings
CountryBulgaria
CityVarna
Period04-06-2204-06-24

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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

Cite this