The role of data choice in data driven identification for online emission models

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{delRe:2011:CIVTS,
  author =       "Luigi {del Re} and Markus Hirsch and 
                 Daniel Alberer and Stephan Winkler",
  title =        "The role of data choice in data driven identification
                 for online emission models",
  booktitle =    "IEEE Symposium on Computational Intelligence in
                 Vehicles and Transportation Systems (CIVTS 2011)",
  year =         "2011",
  month =        "11-15 " # apr,
  address =      "Paris",
  pages =        "46--51",
  size =         "6 pages",
  abstract =     "Data driven models are known to be a valid alternative
                 to first principle approaches for modelling. However,
                 in the case of complex and largely unknown systems such
                 as the chemical reactions leading to engine emissions,
                 experience shows that results from data driven models
                 suffer from a significant dependence on the actual data
                 set used for identification and are prone to an
                 excessive complexity. This paper shows how the use of
                 an incremental design of experiments based on
                 polynomial models can be used to determine the
                 appropriate complexity of the data set as well as a
                 suitable measurement profile which yields an adequate
                 excitation for the model parameter estimation. As this
                 paper shows experimentally, this result is not specific
                 to the particular identification approach used, but the
                 same data set can be used e.g. by genetic programming
                 (GP) algorithms which extract also the model structure
                 from data. Results are shown using emission
                 measurements on a modern turbocharged Diesel engine on
                 an emission test bench.",
  keywords =     "genetic algorithms, genetic programming, chemical
                 reactions, complex systems, data choice, data driven
                 identification, data set, design of experiments,
                 emission measurements, engine emissions, model
                 parameter estimation, modern turbocharged diesel
                 engine, online emission models, polynomial models, air
                 pollution, data models, design of experiments, diesel
                 engines, large-scale systems, mechanical engineering
                 computing, parameter estimation, polynomials",
  DOI =          "doi:10.1109/CIVTS.2011.5949537",
  notes =        "automobile, turbo charged diesel, NOx, particulates,
                 NARX, time lagged inputs. HeuristicLab

                 Also known as \cite{5949537}",
}

Genetic Programming entries for Luigi del Re Markus Hirsch Daniel Alberer Stephan M Winkler

Citations