Comparison and analysis of performance prediction methods for GDI turbocharged engine based on limited data
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Abstract:
In order to reduce the experimental cost of establishing engine performance prediction model, machine learning algorithms with powerful non-linear problem analysis ability were employed to predict the performance of gasoline direct injection (GDI) turbocharged engine, such as general regression neural network (GRNN) and support vector regression (SVR). The Taguchi orthogonal experiment method and the Latin hypercube sampling (LHS) were introduced to determine the operation points of the training data and the test data. And then based on the training models containing the same training data of only 25 samples, the prediction performance of both GRNN and SVR was tested and compared using the same 100 sets of testing data. The comparative analysis shows that GRNN has the risk of converging to the local minimum when the experimental data is limited, whereas SVR can find the optimal global solution with good prediction accuracy and generalization ability. So SVR is very suitable for GDI turbocharged engine performance predictions and significantly reduces experimental costs.
