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Local Gaussian Process Regression in Order to Model Air Charge of Turbocharged Gasoline SI Engines
Technical Paper
2016-01-0624
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.
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Citation
Raidt, B., "Local Gaussian Process Regression in Order to Model Air Charge of Turbocharged Gasoline SI Engines," SAE Technical Paper 2016-01-0624, 2016, https://doi.org/10.4271/2016-01-0624.Also In
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