In this paper we develop a method for learning nonlinear systems with
multiple outputs and inputs. We begin by modelling 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.Comment: 10 pages, 4 figure
