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.
@article{arxiv.1606.04366,
title = {Recursive nonlinear-system identification using latent variables},
author = {Per Mattsson and Dave Zachariah and Petre Stoica},
journal= {arXiv preprint arXiv:1606.04366},
year = {2018}
}