چکیده مقاله
Torque estimation needs intensive efforts and costly sensors In this research, a model was proposed to estimate ITM285 tractor engine torque using some low cost sensors Radial basis function RBF neural network was used for torque estimation, based on the data obtained from some inexpensive sensors including engine speed, exhaust gas opacity, fuel mass flow and exhaust gas temperature Thirteen training algorithms were examined to train the RBF These algorithms were compared using two statistical methods namely k fold cross validation and completely randomized design CRD The Bayesian regularization Trainbr algorithm was the best one in regard of engine torque estimation Based on the sensitivity analysis of the RBF, only using engine speed, fuel mass flow and exhaust gas temperature sensors are sufficient for proper engine torque estimation R2, RMSE and EF of the RBF were 0 99, 0 50 and 0 99, respectively It is concluded that the RBF model can be a suitable technique for estimating engine torque
کلیدواژهها
نویسندگان
Majid Rajabi Vandechali
PhD student, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
Mohammad Hossein Abbaspour-Fard
Professor, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad,Mashhad, Iran
Abbas Rohani
Assistant Professor, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
شیوه ارجاع
Rajabi Vandechali, Majid and Abbaspour-Fard, Mohammad Hossein and Rohani, Abbas,1396,Development a new model based on artificial neural network to estimate torque of a conventional CI engine,2rd International Conference on Soft Computing,Rudsar
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی محاسبات نرم1 آذر 1396 · رودسر