Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set

Ka In Wong, Pak-Kin Wong, Chun Shun Cheung, Chi-Man Vong

Abstract: Highlights•Diesel engine models are built using advanced machine learning techniques and verified based on experimental data.•A new hybrid inference is proposed for the selection of hyperparameters of kernel based extreme learning machine.•Problems of data scarcity and exponentiality are eased by using logarithmic transformation of dependent variables to pre-process the data.•A comparison among the models built by advanced and traditional methods is conducted.•The models developed by advanced methods with the hybrid inference and logarithmic transformation are more accurate than traditional ones.