Recursive nonlinear-system identification using latent variables
This work addresses system identification for nonlinear dynamics, but appears incremental as it builds on existing latent variable and optimization frameworks.
The paper tackles the problem of learning nonlinear systems with multiple inputs and outputs by developing a recursive identification method using latent variables and a convex majorization technique, resulting in parsimonious predictive models tested on synthetic and real data.
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.