Incorporating physical knowledge about the formation of nitric oxides into evolutionary system identification

Stephan Winkler, Markus Hirsch, Michael Affenzeller, Luigi Del Re, Stefan Wagner

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

Genetic programming (GP) is an evolutionary optimization method that has already been used successfully for solving data mining problems in the context of several scientific domains. For example, the identification of models describing the nitric oxides (NOx) emissions of diesel engines has been investigated intensively, very promising results were obtained using GP. In the standard GP process, all model structures (as well as parameter settings) of models are created during an evolutionary process; populations of models are evolved using the genetic operators crossover, mutation and selection. In this paper we discuss several possibilities how a priori knowledge can be integrated into the GP process; we have used physical knowledge about the formation of NOx emissions in a BMW diesel engine, test results are given in the empirical tests section.

Original languageEnglish
Title of host publication20th European Modeling and Simulation Symposium, EMSS 2008
PublisherDIPTEM University of Genova
Pages69-74
Number of pages6
ISBN (Print)8890073268, 9788890073267
Publication statusPublished - 2008
Event20th European Modeling and Simulation Symposium, EMSS 2008 - Campora San Giovanni, Amantea, CS, Italy
Duration: 17 Sep 200819 Sep 2008

Publication series

Name20th European Modeling and Simulation Symposium, EMSS 2008

Conference

Conference20th European Modeling and Simulation Symposium, EMSS 2008
CountryItaly
CityCampora San Giovanni, Amantea, CS
Period17.09.200819.09.2008

Keywords

  • Genetic programming
  • Incorporation of physical knowledge
  • System identification

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