COMPARISON OF GENETIC ALGORITHM AND NEURAL NETWORK APPROACHES FOR THE PROGNOSIS OF MECHANICAL IDLE RUNNING LOSSES IN AGRICULTURE TRACTOR TRANSMISSION

Abstract

An experimental investigation of mechanical idle running losses in an agriculture tractor transmission was used to collect a wide range of data. The influence of the engine rotation speed, the number of switched-on gears, and the oil level in the transmission gearbox on the idle running losses was determined. Adequate regression models in cases of switched-on and switched-off PTO were received. A genetic algorithm was used to optimize mathematical models obtained using regression analysis. A feed-forward artificial neural network was also developed to estimate the same experimental data for mechanical idle running losses in transmission. A back-propagation algorithm was used when training and testing the network. A comparison of the correlation coefficient, reduced chi-square, mean bias error, and root mean square error between the experimental data and fit values of the obtained models was made. It was concluded that the neural network represented the mechanical idle running losses in tractor transmission more accurately than other models.