基于GM(1,1)和线性回归模型在印刷包衬压缩变形中的数据预测研究

Based on GM (1, 1) and the linear regression model in the printing bag lining compression deformation data prediction research

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。

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 val-ue 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 regres-sion model and GM （1, 1） model.