Neural-networks-based nonlinear dynamic modeling for automotive engines

Document Type

Article

Publication Date

1-1-2000

Publication Title

Neurocomputing

Volume

30

Issue

1-4

First Page

129

Keywords

Automotive engine, Complex system modeling, Neural networks, Nonlinear systems

Last Page

142

Abstract

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.

DOI

10.1016/S0925-2312(99)00121-6

ISSN

09252312

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