Name
Title | ||
|---|---|---|
A Sparse Nonstationary Trigonometric Gaussian Process Regression and Its Application on Nitrogen Oxide Prediction of the Diesel Engine |
Abstract | ||
|---|---|---|
Gaussian process regression (GPR) has shown superiority in terms of state estimation for its nonparametric characteristic and uncertainty prediction ability. Due to its heavy computational complexity, GPR is generally used for small datasets. To efficiently deal with the big data, the sparse spectrum approximation method has been successfully applied to GPR to decrease the computational complexity... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TII.2021.3068288 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Kernel,Gaussian processes,Informatics,Computational complexity,Standards,Sparse representation,Diesel engines | Journal | 17 |
Issue | ISSN | Citations |
12 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haojie Huang | 1 | 0 | 0.34 |
| Yedong Song | 2 | 0 | 0.34 |
| Xin Peng | 3 | 25 | 10.91 |
| Steven X. Ding | 4 | 1792 | 124.79 |
| Weimin Zhong | 5 | 79 | 14.18 |
| Wei Du | 6 | 62 | 6.55 |