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

Chi-Hsu Wang*, Jung Sheng Wen

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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 will be 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 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.

Original languageEnglish
Pages878-882
Number of pages5
DOIs
StatePublished - 1 Dec 2005
Event2005 IEEE Networking, Sensing and Control, ICNSC2005 - Tucson, AZ, United States
Duration: 19 Mar 200522 Mar 2005

Conference

Conference2005 IEEE Networking, Sensing and Control, ICNSC2005
CountryUnited States
CityTucson, AZ
Period19/03/0522/03/05

Keywords

  • Engine Control
  • Fuzzy Neural Network
  • Optimal Training
  • Turbo-Charged Engine

Fingerprint Dive into the research topics of 'Self-Learning FNN (SLFNN) with optimal on-line tuning for water injection control in a turbo charged automobile'. Together they form a unique fingerprint.

Cite this