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An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling

Gazi University Journal of Science · Haziran 2022

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YÖKSİS Kayıtları
An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling
GAZI UNIVERSITY JOURNAL OF SCIENCE · 2022 TR DİZİN
DOKTOR ÖĞRETİM ÜYESİ AYNUR YONAR →
An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling
GAZI UNIVERSITY JOURNAL OF SCIENCE · 2022 TR DİZİN
DOKTOR ÖĞRETİM ÜYESİ HARUN YONAR →
An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling
Gazi University Journal of Science · 2022 ESCI
DOKTOR ÖĞRETİM ÜYESİ AYNUR YONAR →

Makale Bilgileri

DergiGazi University Journal of Science
Yayın TarihiHaziran 2022
Cilt / Sayfa35 · 716-729
Erişim🔓 Açık Erişim
Özet Nonlinear regression analysis is an important statistical method widely used in many fields of science to model the complex relationships between variables. Therefore, many studies have been conducted to estimate the parameters of nonlinear regression models using various iterative techniques. In this study, an efficient hybrid algorithm, namely PSONM, by combining the exploration capability of Particle Swarm Optimization (PSO) and the exploitation capability of the Nelder-Mead (NM) algorithm is proposed to obtain parameter estimates of nonlinear regression models. To show the performance of the proposed hybrid algorithm, 20 nonlinear regression tasks with various levels of difficulty, and real data sets in the agriculture field have been tested. The experimental results indicated that the suggested hybrid algorithm provides accurate estimates, and its performance is much superior to those of NM and PSO algorithms.

Yazarlar (2)

1
Aynur Yonar
2
Harun Yonar

Anahtar Kelimeler

Nelder-Mead algorithm Nonlinear regression optimization Parameter estimation Particle swarm

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey