Mostrar el registro sencillo del ítem
dc.contributor.author | Lughofer, Edwin | es_ES |
dc.contributor.author | Macian Martinez, Vicente | es_ES |
dc.contributor.author | Guardiola García, Carlos | es_ES |
dc.contributor.author | Klement, Erich Peter | es_ES |
dc.date.accessioned | 2015-05-19T10:51:16Z | |
dc.date.available | 2015-05-19T10:51:16Z | |
dc.date.issued | 2011-03 | |
dc.identifier.issn | 1568-4946 | |
dc.identifier.uri | http://hdl.handle.net/10251/50488 | |
dc.description.abstract | Antipollution legislation in automotive internal combustion engines requires active control and prediction of pollutant formation and emissions. Predictive emission models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks. In this paper, fuzzy modelling of the NOx emissions of a diesel engine is investigated, which overcomes some drawbacks of pure engine mapping or analytical physical-oriented models. For building up the fuzzy NOx prediction models, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically extracts an appropriate number of rules and fuzzy sets by an evolving version of vector quantization (eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional. The predictive power of the fuzzy NOx prediction models is compared with that one achieved by physical-oriented models based on high-dimensional engine data recorded during steady-state and dynamic engine states. | es_ES |
dc.description.sponsorship | This work was supported by the Upper Austrian Technology and Research Promotion. This publication reflects only the author's view. Furthermore, we acknowledge PSA for providing the engine and partially supporting our investigation. Special thanks are given to PO Calendini, P Gaillard and C. Bares at the Diesel Engine Control Department. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Applied Soft Computing | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Combustion engines | es_ES |
dc.subject | NOx emissions | es_ES |
dc.subject | analytical physical-oriented models | es_ES |
dc.subject | Takagi-Sugeno fuzzy systems | es_ES |
dc.subject | FLEXFIS | es_ES |
dc.subject | High-dimensional data | es_ES |
dc.subject | Steady-state and dynamic engine states | es_ES |
dc.subject.classification | MAQUINAS Y MOTORES TERMICOS | es_ES |
dc.title | Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.asoc.2010.10.004 | |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario CMT-Motores Térmicos - Institut Universitari CMT-Motors Tèrmics | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics | es_ES |
dc.description.bibliographicCitation | Lughofer, E.; Macian Martinez, V.; Guardiola García, C.; Klement, EP. (2011). Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems. Applied Soft Computing. 11(2):2487-2500. doi:10.1016/j.asoc.2010.10.004 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.asoc.2010.10.004 | es_ES |
dc.description.upvformatpinicio | 2487 | es_ES |
dc.description.upvformatpfin | 2500 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 11 | es_ES |
dc.description.issue | 2 | es_ES |
dc.relation.senia | 39199 | |
dc.identifier.eissn | 1872-9681 |