Open AccessAbout the features of the least squares method with linearization when determining nonlinear parameters of functional dependencies
Author(s) -
Lidia V Azarova
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2131/2/022132
Subject(s) - linearization , nonlinear system , non linear least squares , least squares function approximation , mathematics , expression (computer science) , noise (video) , estimation theory , nonlinear regression , computer science , algorithm , statistics , regression analysis , physics , estimator , artificial intelligence , image (mathematics) , programming language , quantum mechanics
The features of approximation of empirical data by functional dependence with nonlinear parameters using the two-stage least squares method are considered in this paper. A method of simplified parameter estimation by constructing a new expression that depends on the parameters in a linear way is described. To obtain the final solution, the least squares estimation of the main dependence linearized in terms of parameters is performed. The influence of various forms of noise imposed on the theoretical dependence on the approximations is modeled.