Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights

被引：3
作者：
Shang, Zhi-Gen [1 ,2 ]
Yan, Hong-Sen [1 ]
机构：
[1] Southeast Univ, Sch Automat, MOE Key Lab Measurement & Control Complex Syst En, Nanjing 210096, Jiangsu, Peoples R China
[2] Yancheng Inst Technol, Dept Automat, Yancheng 224051, Peoples R China
来源：
ENTROPY | 2016年 / 18卷 / 06期
基金：
中国国家自然科学基金;
关键词：
design time forecast; kernel-based regression; Kullback-Leibler divergence; heteroscedasticity; SUPPORT VECTOR MACHINES; PROCESS MODEL; OPTIMIZATION; RISK;
D O I：
10.3390/e18060231
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR.
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页数：17
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