Forecasting Product Design Time Based on Gaussian Margin Regression

摘要：

In order to obtain a forecast model for product design time from a small data set, Gaussian Margin Regression (GMR) is presented on the basis of combining Gaussian Margin Machines and kernel based regression. Gaussian Margin Regression maintains a Gaussian distribution over weight vectors for kernel based regression. The algorithm is applied to seeking the least information distribution that will make actual value be included in the confidential interval with high probability, and embedded genetic algorithm is presented for choosing its relevant parameters. The results of application in injection mold designs reveal that the time forecast model based on GMR is of feasibility and validity.

关键词： design time forecast Gaussian margin regression kernel

作者: Shang Zhigen Yan Hongsen

作者单位: School of Automation,Southeast University,Nanjing 210096,China;Department of Automation,Yancheng Ins School of Automation,Southeast University,Nanjing 210096,China;Key Laboratory of Measurement and Con

会议类型: 国际会议

会议名称: 2011 10th International Conference on Electronic Measurement & Instruments(第十届电子测量与仪器国际会议 ICEMI2011)

会议地点: 成都

会议语种:英文

页码: 1186-1189

在线出版日期: 2011-08-16（万方平台首次上网日期，不代表论文的发表时间）