Development of modeling and control strategies for an approximated Gaussian process
Machine Learning
2020-02-13 v1 Machine Learning
Statistics Theory
Statistics Theory
Abstract
The Gaussian process (GP) model, which has been extensively applied as priors of functions, has demonstrated excellent performance. The specification of a large number of parameters affects the computational efficiency and the feasibility of implementation of a control strategy. We propose a linear model to approximate GPs; this model expands the GP model by a series of basis functions. Several examples and simulation studies are presented to demonstrate the advantages of the proposed method. A control strategy is provided with the proposed linear model.
Cite
@article{arxiv.2002.05105,
title = {Development of modeling and control strategies for an approximated Gaussian process},
author = {Shisheng Cui and Chia-Jung Chang},
journal= {arXiv preprint arXiv:2002.05105},
year = {2020}
}