Hyperparameter Estimation Based on Gaussian Process and its Application in Injection Molding

Abstract:

Article Preview

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.

Info:

Periodical:

Advanced Materials Research (Volumes 328-330)

Edited by:

Liangchi Zhang, Chunliang Zhang and Zichen Chen

Pages:

524-529

DOI:

10.4028/www.scientific.net/AMR.328-330.524

Citation:

J. Y. Ma et al., "Hyperparameter Estimation Based on Gaussian Process and its Application in Injection Molding", Advanced Materials Research, Vols. 328-330, pp. 524-529, 2011

Online since:

September 2011

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Price:

$35.00

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