A comparison of different network based modeling methods for prediction of the torque of a SI engine equipped with variable valve timing

Nowadays, due to increasing the complexity of IC engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. Accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. In this paper four network based modeling methods are used and compared to model the behavior of an IC engine: neural networks model (NN), group method of data handling model (GMDH), a hybrid NN and GMDH model (NN-GMDH), and a GMDH model whose structure is determined by genetic algorithm (Genetic-GMDH). The inputs are engine speed, throttle angle, and intake valve opening and closing timing, and the output is the engine brake torque. Results show that NN has the best prediction capability and Genetic-GMDH model has the most flexible and simplest structure and relatively good prediction ability.