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Zeitschriftenartikel:

Maxime Deregnaucourt, M. Stadlbauer, C. Hametner, S. Jakubek, H. Koegeler:
"Evolving model architecture for custom output range exploration";
Mathematical and Computer Modelling of Dynamical Systems, 21 (2015), 1; S. 1 - 22.



Kurzfassung englisch:
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.

Schlagworte:
online design of experiments, online training, nonlinear system identification, local model networks


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1080/13873954.2014.885056


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.