The Nonlinear System Identification for the Engine of Automated Automobiles Using Neural Networks 961825
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.
Citation: Wu, G., Liu, Q., Song, B., and Zhao, K., "The Nonlinear System Identification for the Engine of Automated Automobiles Using Neural Networks," SAE Technical Paper 961825, 1996, https://doi.org/10.4271/961825. Download Citation
Author(s):
Guangqiang Wu, Qinghe Liu, Bin Song, Keding Zhao
Affiliated:
Harbin Institute of Technology
Pages: 7
Event:
International Off-Highway & Powerplant Congress & Exposition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Cfd and Engine Modeling-SP-1197
Related Topics:
Neural networks
Identification
Throttles
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