| dc.creator | Monti, Cassio Augusto Ussi | |
| dc.creator | Oliveira, Rafael Menali | |
| dc.creator | Roise, Joseph Peter | |
| dc.creator | Scolforo, Henrique Ferraço | |
| dc.creator | Gomide, Lucas Rezende | |
| dc.date | 2023-04-05T18:22:24Z | |
| dc.date | 2023-04-05T18:22:24Z | |
| dc.date | 2022-10-28 | |
| dc.date.accessioned | 2023-09-28T20:07:16Z | |
| dc.date.available | 2023-09-28T20:07:16Z | |
| dc.identifier | MONTI, C. A. U. et al. Hybrid method for fitting nonlinear height? Diameter functions. Forests, [S.l.], v. 13, n. 11, p. 1-18, 2022. DOI: 10.3390/f13111783. | |
| dc.identifier | http://repositorio.ufla.br/jspui/handle/1/56517 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9044936 | |
| dc.description | Regression analysis is widely applied in many fields of science to estimate important
variables. In general, nonlinear regression is a complex optimization problem and presents intrinsic
difficulties in estimating reliable parameters. Nonlinear optimization algorithms commonly require
a precise initial estimate to return reasonable estimates. In this work, we introduce a new hybrid
algorithm based on the association of a genetic algorithm with the Levenberg–Marquardt method
(GALM) to adjust biological nonlinear models without knowledge of initial parameter estimates. The
proposed hybrid algorithm was applied to 12 nonlinear models widely used in forest sciences and
12 databases under varying conditions considering classic hypsometric relationships to evaluate the
robustness of this new approach. The hybrid method involves two stages; the curve approximation
process begins with a genetic algorithm with a modified local search approach. The second stage
involves the application of the Levenberg–Marquardt algorithm. The final performance of the hybrid
method was evaluated using total fitting for all tested models and databases, confirming the reliability
of the proposed algorithm in providing stable parameter estimates. The GA was able to predict the
initial parameters, which assisted the LM in converging efficiently. The developed GALM method is
effective, and its application is recommended for biological nonlinear analyses. | |
| dc.format | application/pdf | |
| dc.language | en_US | |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
| dc.rights | Attribution 4.0 International | |
| dc.rights | acesso aberto | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.source | Forests | |
| dc.subject | Fitting curves | |
| dc.subject | Genetic algorithm | |
| dc.subject | Hybrid method | |
| dc.subject | Hypsometric relationship | |
| dc.subject | Nonlinear parameterization | |
| dc.title | Hybrid method for fitting nonlinear height? Diameter functions | |
| dc.type | Artigo | |
