A Sparse Nonstationary Trigonometric Gaussian Process Regression and its Application on Nitrogen Oxides Prediction of the Diesel Engine
In: IEEE Transactions on Industrial Informatics (T-IINF) / Institute of Electrical and Electronics Engineers (IEEE) (Eds.). , Vol. 17 (2021), No. 12, pp. 8367 - 8377
2021article/chapter in journalClosed access
Mechanical EngineeringFaculty of Engineering » Engineering and Information Technology » Automatic Control and Complex Systems
Title in English:
A Sparse Nonstationary Trigonometric Gaussian Process Regression and its Application on Nitrogen Oxides Prediction of the Diesel Engine
Author:
Huang, Haojie
- ORCID
-
0000-0002-9473-4851
- ORCID
-
0000-0001-9277-8415
- Other
- corresponding author
- GND
- 134302427
- LSF ID
- 2347
- ORCID
-
0000-0002-5149-5918
- Other
- connected with university
- ORCID
-
0000-0002-4285-4739
- Other
- corresponding author
- ORCID
-
0000-0003-3976-2275
Year of publication:
2021
Open Access?:
Closed access
IEEE ID
Web of Science ID
Scopus ID
Language of text:
English
Keyword, Topic:
Computational complexity ; Diesel engines ; Gaussian process regression ; Gaussian processes ; Informatics ; Kernel ; nonstationarity ; sparse Gaussian process regression ; Sparse representation ; Standards
Type of resource:
Text