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Recursive nonlinear-system identification using latent variables
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Electronics.
Department of Information Technology, Uppsala University, Uppsala, Sweden.
Department of Information Technology, Uppsala University, Uppsala, Sweden.
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 93, p. 343-351Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2018. Vol. 93, p. 343-351
Keywords [en]
Nonlinear systems, Multi-input/multi-output systems, System identification
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hig:diva-27382DOI: 10.1016/j.automatica.2018.03.007ISI: 000436916200036Scopus ID: 2-s2.0-85054375676OAI: oai:DiVA.org:hig-27382DiVA, id: diva2:1223428
Funder
Swedish Research Council, 621-2014-5874Swedish Research Council, 2016-06079Available from: 2018-06-25 Created: 2018-06-25 Last updated: 2018-12-05Bibliographically approved

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Mattsson, Per

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  • Other locale
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