[1]鲍 蓉.GM(1,1)和线性回归模型及其在印刷包衬压缩变形数据预测中的应用[J].工业仪表与自动化装置,2014,(05):59-62.
　BAO Rong.GM (1,1) and linear regression model and its applicationin data prediction in printing lining deformation[J].Industrial Instrumentation & Automation,2014,(05):59-62.

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GM(1,1)和线性回归模型及其在印刷包衬压缩变形数据预测中的应用(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:

期数:

2014年05期

页码:

59-62

栏目:

出版日期:

2014-10-15

文章信息/Info

Title:

GM (1,1) and linear regression model and its applicationin data prediction in printing lining deformation

作者:

鲍 蓉

（兰州石化职业技术学院 印刷出版工程系，兰州 730060）

Author(s):

BAO Rong

(Publishing and Printingengineering Department ,Lanzhou Petrochemical College of Vocational Technology, Lanzhou 730060,China?)

关键词:

线性回归; GM(1; 1)模型; 预测; 印刷包衬压缩变形; 压缩变形量

Keywords:

linear regression;  GM (1; 1) model;  prediction;  printed lining compression deformation;  compression deformation

分类号:

O212

DOI:

-

文献标志码:

A

摘要:

运用线性回归对预测数据进行分析，剔除异常数据，用GM(1,1)模型进行预测，有效降低了数据相对误差，提高了预测数据的精度。选用印刷包衬压缩变形的压缩变形量λ值，用线性回归进行数据分析并剔除异常数据后用GM(1,1)进行预测，使得预测数据具有更高的准确性和适应性。实验及仿真结果表明，经过前期数据分析整理后的灰色预测模型，其预测期望值远优于单纯的回归模型和GM(1,1)模型。

Abstract:

In this paper, by using the linear regression in analysis of the predicted data, eliminating abnormal data, using GM (1,1) model to predict, data relative error is reduced and forecast accuracy of data is improved. Selecting compression deformation value of printing lining compression deformation, the data was analyzed with linear regression and abnormal data eliminated to forecast by GM (1,1) to make sure the predicted data with higher accuracy and adaptability. The experimental and simulation results indicate that, after preliminary data analysis, the predicted expection value of grey forecasting model is much better than that of pure regression model and GM (1,1) model.

参考文献/References:

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