SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality

Mohamed Ayeb; Heinz J. Theuerkauf; Thomas Winsel

doi:10.4271/2005-01-0019

2005-04-11

SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality 2005-01-0019

In the last two decades the abilities of neural networks as universal approximation tools of non linear functional relationships as well as identification tools for nonlinear dynamic systems have been recognized and used successfully in many applications areas like modelling, control and diagnosis of technical systems. At the same time an increasing interest in optimal design methods is observed. Design of experiment is used to cope with the growing amount of measurements needed for the calibration of engines due to the rising number of control variables to be considered and the need for more accuracy in the description of engine behaviour to derive the best control strategies.

In this paper a strategy for the integration of the concept of D-optimality in the learning process of neural networks is proposed. This leads to an optimal selection of data to be presented to the training procedure of the neural network aiming to a generation of robust neural models using fewer training data.

An application example dealing with the modelling of the emissions of an SI engine illustrates the successful use of the proposed concept. The generated emissions model is real time capable so that it can be used as a virtual sensor for ECU control and diagnosis functions.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Digital $37.00 Print $37.00

Preview Document

We also recommend:

Standards

SAE Mobilus®

Publications

News

Events

Professional Development

A World in Motion (PreK-12)

Participate with SAE

SAE Membership

Donate

SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality 2005-01-0019

SAE MOBILUS