TY - GEN

T1 - Fuzzy regression analysis by entropy

AU - Kao, Chiang

AU - Lin, Pei Huang

PY - 2004/12/1

Y1 - 2004/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:8844232692

SN - 0780382781

T3 - 2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings

SP - 231

EP - 236

BT - 2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings

A2 - Yager, R.R.

A2 - Sgurev, V.S.

A2 - Jotsov, V.S.

A2 - Koprinkova-Hristova, P.D.

T2 - 2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings

Y2 - 22 June 2004 through 24 June 2004

ER -