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  • 1.
    Mattsson, Per
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics.
    Zachariah, Dave
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Stoica, Petre
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Recursive nonlinear-system identification using latent variables2018In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 93, p. 343-351Article in journal (Refereed)
    Abstract [en]

    In this paper we develop a method for learning nonlinear system models with multiple outputs and inputs. We begin by modeling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization problem is tackled using a majorization–minimization approach. Finally, we develop a convex majorization technique and show that it enables a recursive identification method. The method learns parsimonious predictive models and is tested on both synthetic and real nonlinear systems.

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