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.

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

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