http://www.cnr.it/ontology/cnr/individuo/prodotto/ID283796
ANN-based virtual sensor for on-line prediction of in-cylinder pressure in a diesel engine (Contributo in volume (capitolo o saggio))
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- ANN-based virtual sensor for on-line prediction of in-cylinder pressure in a diesel engine (Contributo in volume (capitolo o saggio)) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/B978-0-444-63456-6.50128-9 (literal)
- Alternative label
Bizon K.; Continillo G.; Lombardi S.; Mancaruso E.; Vaglieco B.M. (2014)
ANN-based virtual sensor for on-line prediction of in-cylinder pressure in a diesel engine
Elsevier B.V., Amsterdam (Belgio) in 24th European Symposium on Computer aided process engineering - part A, 2014
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Bizon K.; Continillo G.; Lombardi S.; Mancaruso E.; Vaglieco B.M. (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
- ESCAPE 24 June 15-18, 2014 - Budapest, Hungary (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.scopus.com/inward/record.url?eid=2-s2.0-84902968808&partnerID=q2rCbXpz (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- 24th European Symposium on Computer aided process engineering - part A (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Bizon K.-Dipartimento di Ingegneria, Università del Sannio,Benevento;
Continillo G.-Dipartimento di Ingegneria, Università del Sannio, Benevento;
Lombardi S. -Dipartimento di Ingegneria, Università del Sannio, Benevento; (literal)
- Titolo
- ANN-based virtual sensor for on-line prediction of in-cylinder pressure in a diesel engine (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-0-444-63434-4 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Jiri Jaromir Klemes, Petar Sabev Varbanov, Peng Yen Liew (literal)
- Abstract
- This study presents the process design and tune-up of robust artificial neural networks (ANN) to be used as virtual sensors for the diagnosis of a three-cylinder Diesel engine operating at various conditions. Particularly, a feed-forward neural network based on radial basis functions (RBF) is employed. The use of different radial basis functions, and their relevant parameters, is investigated in detail, with their effect on the network accuracy. The RBF network is validated using data not included in training, showing good correspondence between measured and reconstructed pressure signal. The accuracy of the predicted pressure signals is analyzed in terms of mean square error and in terms of a number of pressure-derived parameters. Results are promising in terms of performance and accuracy, both for the predicted pressure signals and for the pressure-derived engine parameters that can be used in a closed loop engine control system. © 2014 Elsevier B.V. (literal)
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