The Nonlinear System Identification for the Engine of Automated Automobiles Using Neural Networks

In this paper the nonlinear system identification theory and method using neural networks are presented, the multilayer feedforward networks employed, the backpropagation learning algorithm proposed. The inputs of the networks are consisted of angular velocity and throttle angle, and outputs torque of the engine, finally the comparision of simulation result with that of experiment and other results that embody the effect of system identification are given. Relative studies revealed that the nonlinear system identification for the engine of automated automobiles using neural networks can be effective.