Recursive nonlinear-system identification using latent variables

• Autores: Per Mattsson, Dave Zachariah, Petre Stoica
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 93, 2018, págs. 343-351
• Idioma: inglés
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• Resumen

  • 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.