Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies

DOI10.1016/J.JFRANKLIN.2017.02.006zbMath1395.93435OpenAlexW2587056823MaRDI QIDQ1661433

Xiang Hui Gao, Ka In Wong, Chi-Man Vong, Pak-Kin Wong

Publication date: 16 August 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2017.02.006

zbMATH Keywords

machine learningpoint-by-point engine calibrationsequential design of experiment strategies

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Application models in control theory (93C95)

Related Items (1)

Machine learning and design of experiments with an application to product innovation in the chemical industry

Cites Work

• Ensemble extreme learning machine and sparse representation classification
• Optimization Methods

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