| 1. |
- Hellring, Magnus, et al.
(författare)
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Robust AFR estimation using the ion current and neural networks
- 1999
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Ingår i: SAE transactions. - New York : Society of Automotive Engineers. - 0096-736X. ; 108:03, s. 1585-1589
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Tidskriftsartikel (refereegranskat)abstract
- A robust air/fuel ratio "soft sensor" is presented based on non-linear signal processing of the ion current signal using neural networks. Care is taken to make the system insensitive to amplitude variations, due to e.g. fuel additives, by suitable preprocessing of the signal. The algorithm estimates the air/fuel ratio to within 1.2% from the correct value, defined by a universal exhaust gas oxygen (UEGO) sensor, when tested on steady state test-bench data and using the raw ion current signal. Normalizing the ion current increases robustness but also increases the error by a factor of two. The neural network soft sensor is about 20 times better in the case where the ion current is not normalized, compared with a linear model. On normalized ion currents the neural network model is about 4 times better than the corresponding linear model. Copyright © 1999 Society of Automotive Engineers, Inc.
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| 2. |
- Hellring, Magnus, et al.
(författare)
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Spark advance control using the ion current and neural soft sensors
- 1999
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Ingår i: SAE transactions. - New York : Society of Automotive Engineers. - 0096-736X. ; 108:03, s. 1590-1595
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Tidskriftsartikel (refereegranskat)abstract
- Two spark advance control systems are outlined; both based on feedback from nonlinear neural network soft sensors and ion current detection. One uses an estimate on the location of the pressure peak and the other uses an estimate of the location of the center of combustion. Both quantities are estimated from the ion current signal using neural networks. The estimates are correct within roughly two crank angle degrees when evaluated on a cycle to cycle basis, and roughly within one crank angle degree when the quantities are averaged over consecutive cycles.The pressure peak detection based control system is demonstrated on a SAAB 9000 car, equipped with a 2.3 liter low-pressure turbo charged engine, during normal highway driving. © 1998 Society of Automotive Engineers, Inc.
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| 3. |
- Rögnvaldsson, Thorsteinn
(författare)
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A simple trick for estimating the weight decay parameter
- 1998
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Ingår i: Neural Networks. - Berlin : Springer Berlin/Heidelberg. - 3540653112 ; , s. 71-92
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Konferensbidrag (refereegranskat)abstract
- We present a simple trick to get an approximate estimate of the weight decay parameter lambda. The method combines early stopping and weight decay, into the estimate lambda=parallel to del E(W(es))parallel to/parallel to 2W(es)parallel to, where W(es) is the set of weights at the early stopping point, and E(W) is the training data fit error. The estimate is demonstrated and compared to the standard cross-validation procedure for lambda selection on one synthetic and four real life data sets. The result is that lambda is as good an estimator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute. The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stop ping.
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