| dc.creator |
KARADEDE, Yusuf |
|
| dc.creator |
ÖZDEMİR, Gültekin |
|
| dc.date |
2018-09-30T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:47:08Z |
|
| dc.date.available |
2020-10-06T10:47:08Z |
|
| dc.identifier |
85072d2b-e2cd-4f0d-84b7-a38579cf69cd |
|
| dc.identifier |
10.1007/s00500-018-3413-5 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/85072d2b-e2cd-4f0d-84b7-a38579cf69cd/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/65200 |
|
| dc.description |
The aim of this paper is to present an alternative solution model to estimate the coefficients of large-scaled linear and nonlinear real-life problems due to the fact that least squares and least median squares parameter estimators have some drawbacks when including so many input variables or increased size of the real-world problems. The study presents a hierarchical soft computing model (SOFTC) that consists of three stages. The first stage constitutes a real-valued breeder genetic algorithm (RVBGA). The second stage is constructing a simulated annealing (SA) algorithm in which the best parameter estimation of the RVBGA is selected as its initial point. The third stage is developing a hierarchical soft computing model by using fuzzy recombination method. SOFTC optimizes the best parameter estimations of this algorithms and it provides a trust region for parameter estimation. Three test problems, one of which is linear and others are nonlinear, are used to examine robustness of proposed models. SOFTC, RVBGA_SA and RVBGA algorithms performed the best parameter estimations, respectively, for the three test problems. The results which are discussed in detail are promising for future usage of these algorithms. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
A hierarchical soft computing model for parameter estimation of curve fitting problems |
|
| dc.type |
info:eu-repo/semantics/article |
|