Name
Abstract | ||
|---|---|---|
The goal of this paper is to handle the large variation issues in fuzzy data by constructing a variable spread multivariate adaptive regression splines (MARS) fuzzy regression model with crisp parameters estimation and fuzzy error terms. It deals with imprecise measurement of response variable and crisp measurement of explanatory variables. The proposed method is a two-phase procedure which applies the MARS technique at phase one and an optimization problem at phase two to estimate the center and fuzziness of the response variable. The proposed method, therefore, handles two problems simultaneously: the problem of large variation issue and the problem of variation spreads in fuzzy observations. A realistic application of the proposed method is also presented, by which the suspended load is modeled using discharge in a hydrology engineering problem. Empirical results demonstrate that the proposed approach is more efficient and more realistic than some well-known least-squares fuzzy regression models. (C) 2014 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2014.09.010 | Applied Soft Computing |
Keywords | Field | DocType |
Discharge,Fuzzy regression,Hydrology,Multivariate adaptive regression splines (MARS),Spline basis function,Suspended load | Fuzzy regression model,Multivariate adaptive regression splines,Hydrology,Regression analysis,Suspended load,Fuzzy regression,Artificial intelligence,Optimization problem,Mars Exploration Program,Mathematical optimization,Fuzzy logic,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
25 | C | 1568-4946 |
Citations | PageRank | References |
7 | 0.48 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jalal Chachi | 1 | 23 | 3.44 |
| S. Mahmoud Taheri | 2 | 90 | 10.84 |
| Naser Reza Arghami | 3 | 15 | 2.46 |