Total least squares estimation model based on uncertainty theory

被引：6
|
作者：
Shi, Hongmei [1 ]
Sun, Xiangqun [1 ]
Wang, Shuai [2 ,3 ]
Ning, Yufu [2 ,3 ]
机构：
[1] Shandong Agr & Engn Univ, Sch Informat Sci & Engn, Nongganyuan Rd, Jinan 250100, Shandong, Peoples R China
[2] Shandong Youth Univ Polit Sci, Sch Informat Engn, Jingshi East Rd, Jinan 250103, Shandong, Peoples R China
[3] Key Lab Intelligent Informat Proc Technol & Secur, Jinan 250103, Shandong, Peoples R China
来源：
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING | 2022年 / 14卷 / 8期
关键词：
Total least squares estimation; Least squares estimation; Uncertainty theory; Linear regression model; LINEAR-REGRESSION MODEL;
D O I：
10.1007/s12652-021-03671-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Uncertain least squares estimation is one of the important methods to deal with imprecisely observed data, which can solve linear regression equation effectively. However, the least squares estimation does not consider the influence of the error of the given independent variables data on the linear regression equation during the regression analysis. Based on the least squares estimation and uncertainty theory, this paper proposed the uncertain total least squares estimation of linear regression model. The total least squares estimation first corrects the data of the given independent variables and make the given data more precise, then solve for the expected value of the square of each of the residual, and minimize the sum of the expected values, so the regression equation obtained is more reasonable and reliable. Numerical example verify the feasibility of the total least squares estimation, and the data analysis shows that the fitting effect of the linear regression equation is good.
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页码：10069 / 10075
页数：7
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