Comparative Studies of Model Performance Based on Different Data Sampling Methods

摘要：

　　This paper presents a comparative study on the effects of different data sampling methods to the performance of data-driven models.An engineering benchmark modeling problem is investigated,focused on which,three sampling methods,i.e.orthogonal Latin sampling,uniform design sampling and random sampling are used to generate the training data of different property.Six typical data-driven modeling techniques,which consist of artificial intelligent methods (least squares support vector machine,BP neural network and RBF neural network) and statistical methods (multiple linear regression,linear and nonlinear partial least squares regressions),are performed to make the comparison.The root mean square error (RMSE),R square (R2) and mean relative error (MRE) values are taken as the comparison criteria.The results reveal that data sampling and data property play a very key role in establishing an accurate data-driven model.

关键词： orthogonal Latin sampling, uniform design data-driven model least squares support vector machine artificial neural network partial least squares

作者: You Lv Jizhen Liu Tingting Yang

作者单位: The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University, Changping, Beijing 102206, China

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 2731-2735

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）