Data from: Compressor map regression modelling based on partial least squares

In this work, two kinds of partial least squares modelling methods are
applied to predict a compressor map: one uses a Power function polynomial
as the basis function (PLSO), and the other uses a trigonometric function
polynomial (PLSN). To demonstrate the potential capabilities of PLSO and
PLSN for a typical interpolated prediction and extrapolated prediction,
they are compared with two other classical data-driven modelling methods,
namely, the look-up table and artificial neural network. PLSO and PLSN are
also compared to each other. The results show that PLSO and PLSN have a
better prediction performance than the look-up table and the artificial
neural network, especially for the extrapolated prediction. At the same
time, the computational time is also decreased sharply. Compared with
PLSO, PLSN is characterized with higher prediction accuracy and shorter
computational time than PLSO. It can be expected that PLSN can be
time-saving and improve the accuracy of a thermodynamic model of a diesel
engine.