SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality Institute of Electrical Power Engineering - Drive Engineering (IEE-AT), University of Kassel

Format:

Conference/Event

Author/Creator:

Ayeb, Mohamed, author.

Conference Name:

SAE 2005 World Congress & Exhibition (2005-04-11 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2005

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2005-01-0019

Access Restriction:

Restricted for use by site license