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- W2394396033 abstract "Because of the total least squares approximation simultaneously considered the errors both in the observation vector and the coefficient matrix,the theory was more rigorously than standard least squares.Nonlinear total least squares adjustment,an extended model of total least squares,where some elements of the coefficient matrix might be a function depending on the observation vector(input vector),has been discussed.In this contribution,apossible iterative algorithm which is based on the Gauss-Newton algorithm and derived by Lagrange-multiplier approach was designed for nonlinear total least squares adjustment.Two numerical experiments are given at last,one on the nonlinear regression and another one on the nonlinear fitting,to demonstrate the validation and the applicability of the suggested algorithm.The results shows that the estimated parameter from nonlinear total least squares adjustment algorithm is closer to the truth-value than classical least squares either in the equal or unequal weights case,and the estimated variance is also almost consistent with the priori value." @default.
- W2394396033 created "2016-06-24" @default.
- W2394396033 creator A5054879862 @default.
- W2394396033 date "2014-01-01" @default.
- W2394396033 modified "2023-09-23" @default.
- W2394396033 title "An Iterative Algorithm for Nonlinear Total Least Squares Adjustment" @default.
- W2394396033 hasPublicationYear "2014" @default.
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