1 resultado para Non-perturbative methods
em Bucknell University Digital Commons - Pensilvania - USA
Filtro por publicador
- Aberystwyth University Repository - Reino Unido (2)
- Academic Archive On-line (Jönköping University; Sweden) (1)
- Academic Research Repository at Institute of Developing Economies (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (7)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (3)
- Aquatic Commons (6)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (12)
- Archive of European Integration (4)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (6)
- Aston University Research Archive (17)
- B-Digital - Universidade Fernando Pessoa - Portugal (1)
- Biblioteca de Teses e Dissertações da USP (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (6)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (27)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (4)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (34)
- Boston University Digital Common (4)
- Brock University, Canada (3)
- Bucknell University Digital Commons - Pensilvania - USA (1)
- CaltechTHESIS (7)
- Cambridge University Engineering Department Publications Database (25)
- CentAUR: Central Archive University of Reading - UK (56)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (22)
- Cochin University of Science & Technology (CUSAT), India (9)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- Dalarna University College Electronic Archive (8)
- Deakin Research Online - Australia (70)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Commons - Michigan Tech (1)
- Digital Commons @ DU | University of Denver Research (2)
- Digital Commons at Florida International University (2)
- DigitalCommons@The Texas Medical Center (1)
- DigitalCommons@University of Nebraska - Lincoln (2)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (11)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (8)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Greenwich Academic Literature Archive - UK (7)
- Helda - Digital Repository of University of Helsinki (25)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Indian Institute of Science - Bangalore - Índia (60)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Politécnico de Viseu (1)
- Instituto Politécnico do Porto, Portugal (5)
- Instituto Superior de Psicologia Aplicada - Lisboa (1)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Massachusetts Institute of Technology (1)
- Nottingham eTheses (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (106)
- Queensland University of Technology - ePrints Archive (187)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (1)
- Repositório Científico da Universidade de Évora - Portugal (3)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (1)
- Repositório do Centro Hospitalar de Lisboa Central, EPE - Centro Hospitalar de Lisboa Central, EPE, Portugal (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositório Institucional da Universidade Federal do Rio Grande - FURG (1)
- Repositório Institucional da Universidade Tecnológica Federal do Paraná (RIUT) (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (63)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (3)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (24)
- Universidade de Lisboa - Repositório Aberto (4)
- Universidade Federal do Pará (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universidade Técnica de Lisboa (1)
- Universita di Parma (1)
- Universitat de Girona, Spain (1)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (5)
- Université de Lausanne, Switzerland (13)
- Université de Montréal (1)
- Université de Montréal, Canada (40)
- Université Laval Mémoires et thèses électroniques (1)
- University of Michigan (2)
- University of Queensland eSpace - Australia (3)
- University of Washington (3)
- WestminsterResearch - UK (3)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.