Name
Abstract | ||
|---|---|---|
A novel approach is introduced to construct a fuzzy regression model when both input data and output data are interval-valued fuzzy numbers. Using a distance on the space of interval-valued fuzzy numbers, a least-squares method is developed. Also, a nonlinear programming model is proposed to estimate the crisp parameters for the interval-valued fuzzy regression model. A real example demonstrates the feasibility and efficiency of the proposed method. Moreover, two goodness of fit indices are introduced and employed for more evaluation of such fuzzy interval-valued regression models. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FUZZ-IEEE.2013.6622315 | 2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013) |
Keywords | Field | DocType |
Interval-valued fuzzy number, fuzzy regression, least-squares method, goodness of fit | Mathematical optimization,Fuzzy classification,Defuzzification,Fuzzy set operations,Fuzzy logic,Fuzzy mathematics,Fuzzy set,Artificial intelligence,Fuzzy number,Membership function,Machine learning,Mathematics | Conference |
ISSN | Citations | PageRank |
1098-7584 | 2 | 0.38 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Reza Rabiei | 1 | 6 | 1.47 |
| Naser Reza Arghami | 2 | 15 | 2.46 |
| S. Mahmoud Taheri | 3 | 90 | 10.84 |
| Bahram Sadeghpour Gildeh | 4 | 35 | 8.99 |