NDLI: Improved control of electronic diesel injection by means of neural networks

Content Provider	IEEE Xplore Digital Library
Author	Herrero, J.J. Capella, J.V. Ors, R.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Eng., Valencia Polytech Univ., Spain (Herrero, J.J.; Capella, J.V.; Ors, R.)
Abstract	The search of more power and softness, as well as bigger efficiency in the combustion, smaller fuel consumption and emission of polluting gases in the current engines, demands more and more complex and critical control systems. In this work an improvement of the electronic injection, based at the moment on cartographic methods, by means of the application of neural networks is presented. The neural networks allow to adjust the values obtained to each exact point of operation, forgetting the intermediate steps of the previous methods or another approach that are not adjusted to the ideal function. In addition the proposal reduces considerably the number of data that are necessary to store for very exact controls based on cartographies pre-calculated. On the other hand this approach opens the doors to intelligent systems in the own injection control, that by means of self-learning, will obtain improvements in the methods to obtain the data of the engine operation and their direct and instantaneous interaction on the engine. In this manner, it is possible to achieve that the injection be accurate during all the engine lifetime.
Starting Page	1488
Ending Page	1493
File Size	1121775
Page Count	6
File Format	PDF
ISBN	0780386620
DOI	10.1109/ICIT.2004.1490784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-08
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Diesel engines Control systems Fuels Computer networks Temperature measurement Power engineering computing Power engineering and energy Electronic mail Combustion
Content Type	Text
Resource Type	Article

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