A supervised neural computing representation predicts obstacle induced force incitement in off-road vehicles

In this paper an attempt has been made to evaluate and predict obstacle induced force incitement in off-road vehicles affected by inflation pressure, wheel load, obstacle type and height, soil texture, tire type, slippage and velocity using artificial neural network ( ANN ) technique trained by back propagation algorithms from readily available data obtained from experiments conducted in soil bin facility and a single wheel-tester. A total of 6912 samples were available for training, validating and testing the neural networks. Evaluating a three layered architecture with a four layered one, the optimal topology to yield better performance on the criteria of lower root mean squared error ( MSE ), T value and coefficient of determination ( R 2 ) was a four-layered one. Then among 2 hidden layers, each of layers was increased from 0 to 40 to determine the best number of neurons by the best performance. It was divulged that 8-12-10-2 provided the best performance of MSE and T were 0.027, 0.977, and R 2 with, respectively.