基于总最小二乘的改进灰色模型gm(1,1)算法及其在变形预测中的应用

Lu Tieding, Zhou Shijian, Liu Wei, Zhang Liting
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引用次数: 4

摘要

提出了一种改进的基于总最小二乘(TLS)的灰色模型gm(1,1)算法。我们知道灰色模型gm(1,1)中的参数a和b可以用最小二乘法求解。LS方法基于一个假设,即向量Y包含误差,而重复加性矩阵B在GM(1,1)中是精确的。当我们分析矩阵B的元素时,矩阵B实际上也包含误差。TLS是适用于向量Y和矩阵b均存在误差时的拟合方法。实例计算结果表明,基于TLS的预测模型可以提高预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved algorithm of grey model-GM(1,1) based on total least squares and its application in deformation forecast
This paper presents an improved algorithm for grey model-GM(1,1) based on total least squares(TLS). As we know that the parameters a and b in grey model-GM(1,1) can be solved by the Least squares method. The LS method is based on an assumption that vector Y contains errors while repeated additive matrix B is accurate in GM(1,1). When we analyze the element of matrix B, the matrix B also contains errors in fact. TLS is the method of fitting that is appropriate when there are errors in both vector Y and matrix B. The calculated results of an example show that the prediction model based on TLS can enhance the prediction accuracy.
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