A multivariate statistical combination forecasting method for product quality evaluation

被引:65
作者
Yin, Shen [1 ,3 ]
Liu, Lei [2 ]
Hou, Jian [1 ]
机构
[1] Bohai Univ, Coll Engn, Jinzhou 121013, Peoples R China
[2] Bohai Univ, Coll Math & Phys, Jinzhou 121013, Peoples R China
[3] Harbin Inst Technol, Sch Astronaut, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
Prediction; Combination forecasting; Quality evaluation; Multivariate statistical methods; SYSTEMS; DESIGN;
D O I
10.1016/j.ins.2016.03.035
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a multivariate statistical combination forecasting method is proposed for key performance evaluation in process industry. This method is developed based on some of the most popular multivariate statistic approaches. It merges the advantages of the principal component regression method (PCR), the partial least squares regression method (PLSR) and the modified partial least squares regression method (MPLSR). We test the proposed method with a numerical example and also an actual wine production process. The results indicate that the prediction accuracy of the optimal combination forecasting method is superior to those of individual methods. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:229 / 236
页数:8
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