Difference between revisions of "Publications:Strategies for handling the fuel additive problem in neural network based ion current interpretation"
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| − | {{PublicationSetupTemplate|PID=327095 | + | {{PublicationSetupTemplate|Author=Stefan Byttner, Thorsteinn Rögnvaldsson, Nicholas Wickström |
| − | |Name=Byttner, Stefan | + | |PID=327095 |
| + | |Name=Byttner, Stefan (stefan) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Rögnvaldsson, Thorsteinn (denni) (0000-0001-5163-2997) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Wickström, Nicholas (nicholas) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650)) | ||
|Title=Strategies for handling the fuel additive problem in neural network based ion current interpretation | |Title=Strategies for handling the fuel additive problem in neural network based ion current interpretation | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
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| Title | Strategies for handling the fuel additive problem in neural network based ion current interpretation |
|---|---|
| Author | |
| Year | 2001 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | |
| Conference | SAE 2001 World Congress, Session: Electronic Engine Controls (Part C&D), Detroit, MI, USA, 5-8 March, 2001 |
| DOI | http://dx.doi.org/10.4271/2001-01-0560 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:327095 |
| Abstract | With the introduction of unleaded gasoline, special fuel agents have appeared on the market for lubricating and cleaning the valve seats. These fuel agents often contain alkali metals that have a significant impact on the ion current signal, thus affecting strategies that use the ion current for engine control and diagnosis, e.g., for estimating the location of the pressure peak. This paper introduces a method for making neural network algorithms robust to expected disturbances in the input signal and demonstrates how well this method applies to the case of disturbances to the ion current signal due to fuel additives containing sodium. The performance of the neural estimators is compared to a Gaussian fit algorithm, which they outperform. It is also shown that using a fuel additive significantly improves the estimation of the location of the pressure peak. © 2001 Society of Automotive Engineers, Inc. |
