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Evolving model architecture for custom output range exploration

Author

Listed:
  • Maxime Deregnaucourt
  • Markus Stadlbauer
  • Christoph Hametner
  • Stefan Jakubek
  • Hans-Michael Koegeler

Abstract

In this paper, a methodology for combined online design of experiments and system identification is presented. More specifically, the paper addresses the problem of creating a model automatically that describes an unknown process accurately in a predefined range of its output. Such a model is typically needed for the calibration of combustion engines where only a relatively small emission range is of interest. The presented solution approach consists of two interacting components: first, an evolving local model network is used for creating, refining and extending a data-driven model, based on the incoming measurements; second, model-based approaches are proposed for designing new experiments so that the data-driven model has a high degree of accuracy in a predefined range of its output. The method uses, besides the models, a space-filling to explore untrained areas. The proposed concepts are illustrated and discussed by means of an academic and two real-world examples.

Suggested Citation

  • Maxime Deregnaucourt & Markus Stadlbauer & Christoph Hametner & Stefan Jakubek & Hans-Michael Koegeler, 2015. "Evolving model architecture for custom output range exploration," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 21(1), pages 1-22, January.
    • Handle: RePEc:taf:nmcmxx:v:21:y:2015:i:1:p:1-22
    DOI: 10.1080/13873954.2014.885056
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