测绘通报 ›› 2018, Vol. 0 ›› Issue (11): 121-125.doi: 10.13474/j.cnki.11-2246.2018.0364

• 技术交流 • 上一篇    下一篇

一种基于MTLS优化的非等间距多点灰色变形预测模型

甘祥前1,2, 任超1,2, 刘林波1,2, 刘中流1,2, 杨庆1,2   

  1. 1. 桂林理工大学测绘地理信息学院, 广西 桂林 541004;
    2. 广西空间信息与测绘重点实验室, 广西 桂林 541004
  • 收稿日期:2017-11-23 修回日期:2018-09-17 出版日期:2018-11-25 发布日期:2018-11-29
  • 通讯作者: 任超。E-mail:renchao@glut.edu.cn E-mail:renchao@glut.edu.cn
  • 作者简介:甘祥前(1991-),男,硕士生,主要研究方向为精密工程测量及变形数据处理。E-mail:593615631@qq.com
  • 基金资助:
    国家自然科学基金地区科学基金(41461089);整体最小二乘及变形监测项目(YCSW2017155);广西科技厅自然科学基金(2014GXNSFAA118288);广西空间信息与测绘重点实验室项目(16-380-25-22)

An Non-equidistant Multi-point Gray Deformation Prediction Model Optimized by MTLS

GAN Xiangqian1,2, REN Chao1,2, LIU Linbo1,2, LIU Zhongliu1,2, YANG Qing1,2   

  1. 1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China;
    2. Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China
  • Received:2017-11-23 Revised:2018-09-17 Online:2018-11-25 Published:2018-11-29

摘要: 针对非等间距多点灰色变形预测模型中观测值矩阵和背景值矩阵都存在一定的误差这一现象,提出了一种基于多元整体最小二乘优化的非等间距多点灰色变形预测模型。通过结合实例分析,结果表明:相比于只考虑观测值存在误差的最小二乘参数估计的非等间距多点灰色变形预测模型,该模型可以抑制建模数据误差,提高模型的拟合及预测精度,适合在变形预测中应用。

关键词: 非等间距多点灰色模型, 多元整体最小二乘, 最小二乘, 变形预测

Abstract: For there are some errors in the observation matrix and background value matrix of the non-equidistant multi-point gray deformation prediction model, an optimization non-equidistant multi-point gray deformation prediction model based on multivariate total least-squares is proposed. By combining the example analysis, the results show that compared to non-equidistant multi-point gray deformation prediction model based on least-squares parameter estimation that only considers the observed value exists error, this model can suppress the error of modeling data, and improve the fitting and prediction accuracy of the model. It is suitable for application in deformation prediction.

Key words: non-equidistant multi-point gray model, multivariatetotal least-squares, least-squares, deformation prediction

中图分类号: