TY - JOUR
ID - 2596
TI - Multiple Fuzzy Regression Model for Fuzzy Input-Output Data
JO - Iranian Journal of Fuzzy Systems
JA - IJFS
LA - en
SN - 1735-0654
AU - Chachi, Jalal
AU - Taheri, S. Mahmoud
AD - Department of Mathematics, Statistics and Computer Sciences, Sem-
nan University, Semnan, Semnan 35195-363, Iran
AD - Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, P.O. Box 11365-4563, Iran
Y1 - 2016
PY - 2016
VL - 13
IS - 4
SP - 63
EP - 78
KW - Fuzzy regression
KW - Interval-valued regression
KW - Least squares method
KW - $LR$-Fuzzy number
KW - Multiple regression
KW - Predictive ability
DO - 10.22111/ijfs.2016.2596
N2 - A novel approach to the problem of regression modeling for fuzzy input-output data is introduced.In order to estimate the parameters of the model, a distance on the space of interval-valued quantities is employed.By minimizing the sum of squared errors, a class of regression models is derived based on the interval-valued data obtained from the $alpha$-level sets of fuzzy input-output data.Then, by integrating the obtained parameters of the interval-valued regression models, the optimal values of parameters for the main fuzzy regression model are estimated.Numerical examples and comparison studies are given to clarify the proposed procedure, and to show the performance of the proposed procedure with respect to some common methods.
UR - http://ijfs.usb.ac.ir/article_2596.html
L1 - http://ijfs.usb.ac.ir/article_2596_c5d1e02ec07e74c58799b657496f0c39.pdf
ER -