Title :
Modeling Automotive Engine Control Module with Neural Network Trained by Iterated Kalman Filter Algorithm
Author :
Sun, Pu ; Marko, Kenneth ; Huang, Yaqi
Author_Institution :
ETAS, Ann Arbor
Abstract :
An attempt has been made to model a production engine control module. Both extended Kalman filter (EKF) algorithm and iterated extended Kalman filter (IEKF) algorithm are used in the construction of the model. The results shows the model trained by both algorithms can produce accurate results with RMS errors in a range of 2- 3%, while iterated extended Kalman filter algorithm outperforms the extended Kalman filter algorithm
Keywords :
Kalman filters; automotive engineering; iterative methods; learning (artificial intelligence); neural nets; nonlinear filters; automotive engine control module modeling; iterated extended Kalman filter algorithm; neural network training; production engine control module; Automotive engineering; Covariance matrix; Electrochemical machining; Engine cylinders; Equations; Neural networks; Production; Sparks; Temperature sensors; Vehicles;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.477
