Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7108673 | Automatica | 2018 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Per Mattsson, Dave Zachariah, Petre Stoica,