Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work.

► In this study, we modeled gasoline engine performance and emission parameters with fuzzy expert system (FES) and artificial neural network (ANN).
► ANN and FES approach has been applied comparatively for predicting engine power, torque, specific fuel consumption, and emission of hydrocarbon.
► The correlation coefficients are resulted 0.99938 and 0.99965 for experiment-ANN and experiment-FES respectively. Also, mean absolute percentage accuracies are 0.9622 for ANN and 0.9743 for FES.
► Unperformed experiments are predicted with developed systems for engine performance and emission parameters.
► Consequently, with the use of ANN and FES, the performance and exhaust emissions of the internal combustion engines can easily be estimated.