NDLI: Self-learning FNN (SLFNN) with optimal on-line tuning for water injection control in a turbo charged automobile

Content Provider	IEEE Xplore Digital Library
Author	Chi-Hsu Wang Jung-Sheng Wen
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Chi-Hsu Wang; Jung-Sheng Wen)
Abstract	This paper proposes a new architecture of self-learning fuzzy-neural-network (SLFNN) for water injection control in a turbo-charged automobile. The major advantage of SLFNN is that no off-line training is needed for initialization. The SLFNN will initialize itself with a random set of initial weighting factors (normally zeros) and a specifically designed on-line optimal training algorithm is invoked immediately after the engine of the automobile is turn on. The on-line optimal training can guarantee that the weighting factors will be directed toward a maximum-error-reduced direction. Although this SLFNN can also be used as a controller for fuel injection, we adopt the SLFNN as the water injection controller to reduce the knocking effects of a turbo-charged engine and therefore the emission is cleaner with less petrol consumption. Real implementation has been performed in a Saab NG 900 (1994 -1998) automobile with excellent results.
Starting Page	878
Ending Page	882
File Size	801667
Page Count	5
File Format	PDF
ISBN	0780388127
DOI	10.1109/ICNSC.2005.1461308
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Optimal control Automobiles Engines Fuzzy neural networks Fuels Petroleum Control engineering Neural networks Ignition
Content Type	Text
Resource Type	Article

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