TY - GEN
T1 - A fuzzy regression model for predicting non-crisp variable
AU - Wang, Huaitien
AU - Pan, Nang Fei
PY - 2008
Y1 - 2008
N2 - Ordinary regression analysis is one of the most powerful approaches for the applications in engineering predictions. However, ordinary regression techniques are incapable of analyzing non-crisp or fuzzy observed data. This paper presents a matrixdriven multiple fuzzy linear regression model. The proposed model can deal with a mixture of fuzzy data and crisp data. An illustrative example is presented to illustrate the use of the proposed model. The result shows the capability of the proposed model.
AB - Ordinary regression analysis is one of the most powerful approaches for the applications in engineering predictions. However, ordinary regression techniques are incapable of analyzing non-crisp or fuzzy observed data. This paper presents a matrixdriven multiple fuzzy linear regression model. The proposed model can deal with a mixture of fuzzy data and crisp data. An illustrative example is presented to illustrate the use of the proposed model. The result shows the capability of the proposed model.
UR - https://www.scopus.com/pages/publications/58149114450
UR - https://www.scopus.com/pages/publications/58149114450#tab=citedBy
U2 - 10.1109/FSKD.2008.296
DO - 10.1109/FSKD.2008.296
M3 - Conference contribution
AN - SCOPUS:58149114450
SN - 9780769533056
T3 - Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
SP - 104
EP - 106
BT - Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
T2 - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
Y2 - 18 October 2008 through 20 October 2008
ER -