| Issue |
MATEC Web Conf.
Volume 63, 2016
2016 International Conference on Mechatronics, Manufacturing and Materials Engineering (MMME 2016)
|
|
|---|---|---|
| Article Number | 05033 | |
| Number of page(s) | 5 | |
| Section | Computer Engineering and Applications | |
| DOI | https://doi.org/10.1051/matecconf/20166305033 | |
| Published online | 12 July 2016 | |
Study on the Curvature Reducing Method of Non-linear Regression Model
China Satellite Maritime Tracking and Controlling Department, Jiangyin 214413, Jiangsu, China
The method to reduce the non-linear strength (curvature)of non-linear regression model was studied in this paper. Firstly, the reference point of the non-linear strength was analyzed. Based on the definition of curvature cubic matrix, a computing method of curvature cubic matrix was proposed based on the Cholesky disassembling. Then the common ways to reduce the non-linear strength was also discussed. Pointed at some common non-linear models in real engineering applications, such as non-linear models used for multiple-measurement and mutual-calibration of different instruments, or non-linear models prior information, a new least square method with weight was given, which can evidently reduce the curvature of these multi-structure non-linear regression models, therefore evidently reduce the non-linear strength. Finally, the Numerical simulation results were given to validate the effectiveness and feasibility of this weighted least square method. The method to reduce the non-linear strength (curvature) of non-linear regression model was studied in this paper. Firstly, the reference point of the non-linear strength was analyzed. Based on the definition of curvature cubic matrix, a computing method of curvature cubic matrix was proposed based on the Cholesky disassembling. Then the common ways to reduce the non-linear strength was also discussed. Pointed at some common non-linear models in real engineering applications, such as non-linear models used for multiple-measurement and mutual-calibration of different instruments, or non-linear models with prior informations, a new least square method with weight was given, which can evidently reduce the curvature of these multi-structure non-linear regression models, therefore evidently reduce the non-linear strength. Finally, the Numerical simulation results ware given to validated the effectiveness and feasibility of this weighted least square method.
Key words: Non-linear regression / curvature / cholesky disassembling / least square with weight
© Owned by the authors, published by EDP Sciences, 2016
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