Hyperparameter Estimation Based on Gaussian Process and its Application in Injection Molding
As a powerful modeling tool, Gaussian process (GP) employs a Bayesian statistics approach and adopts a highly nonlinear regression technique for general scientific and engineering tasks. In the first step of constructing Gaussian process model is to estimate the best value of the hyperparameter which turned to be used in the second step where a satisfactory nonlinear model was fitted. In this paper, a modified Wolfe line search approach for hyperparameters estimation by maximizing the marginal likelihood based on conjugate gradient method is proposed. And then we analyze parameter correlation according to the value of hyperparameters to control the warpage which is a main defect for a thin shell structure part in injection molding.
Gaussian Process Hyperparameter Estimation Modified Wolfe Line Search Parameter Correlation lnjection Molding
Junyan Ma Xiaoping Liao Wei Xia Xuelian Yan
School of Mechanical and Engineering, Guangxi University, Nanning, Guangxi Province, China
国际会议
2011 International Conference on Mechatronics and Materials Processing(2011年机电一体化与材料加工国际会议 ICMMP)
广州
英文
524-529
2011-11-18(万方平台首次上网日期,不代表论文的发表时间)