Improving nonlinear regression model estimation based on Coati Optimization Algorithm
Keywords:
nonlinear regression; parameter estimation, nonlinear least squares estimation, Maximum Likelihood Estimation (MLE), Coyote Optimization Algorithm (COA)
Abstract
The mathematical and social sciences together with engineering fields use Non-Linear Regression analysis as one of their primary techniques. Controls and modeling of Non-Linear systems rely heavily on parameters estimation as a crucial problem. This paper presents a brief examination of this issue and develops an effective COA algorithm for parameter estimation accuracy enhancement of six Non-Linear Regression models (Negative exponential model, Model based on logistics, Chwirut1 model , Hougen-Watson model, Dan Wood model , and Sigmoid model). Simulation tests showed that the Maximum Likelihood Estimation (MLE) method using the Coyote Optimization Algorithm (COA) achieved the best performance when selecting among different methods along with different samples sizes and the mean squared error criterion.
Published
2025-07-04
How to Cite
Salim, O., & Algamal, Z. Y. (2025). Improving nonlinear regression model estimation based on Coati Optimization Algorithm. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2563
Issue
Section
Research Articles
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