fa موتور احتراق داخلی Internal Combustion Engines (ICE, ...) پژوهشي Research <p class="Abstract" style="margin: 0in 0in 10pt"><font size="2"><font color="#000000"><font face="Times New Roman">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.</font></font></font></p> <p class="Abstract" style="margin: 0in 0in 10pt"><font size="2"><font color="#000000"><font face="Times New Roman">.</font></font></font></p> Neural networks, Group method of data handling, Engine torque, Black box modeling, Variable valve timing 2082 2096 http://ase.iust.ac.ir/browse.php?a_code=A-10-63-115&slc_lang=fa&sid=1 A.H. Kakaee akakaee@iust.ac.ir 180031947532846001413 180031947532846001413 Yes Assistant Professor, School of Automotive Engineeringt, Iran University of Science and Technology B. Mashhadi 180031947532846001414 180031947532846001414 No School of Automotive Engineeringt, Iran University of Science and Technology M. Ghajar 180031947532846001415 180031947532846001415 No Faculty of Automotive Engineering