Neural-networks-based nonlinear dynamic modeling for automotive engines

This paper presents a procedure for using neural networks to identify the nonlinear dynamic model of the intake manifold and the throttle body processes in an automotive engine. A dynamic neural network called external recurrent neural network, is used for dynamic mapping and model construction. Dynamic Levenberg-Marquardt algorithm is then applied to the weight-estimation problem. Modeling results indicate that the neural-network-based models have a rather simple structure. Early results also confirm that the neural-network-based modeling of the manifold dynamics can result in a model that is comparable if not better than the first-principle-based models. In addition, it was verified that the neural model has good generalization capabilities. (C) 2000 Elsevier Science B.V. All rights reserved.