Recursive nonlinear-system identification using latent variables

TL;DR

This paper introduces a recursive method for identifying complex nonlinear systems with multiple inputs and outputs by modeling errors with latent variables and optimizing via majorization-minimization, demonstrated on synthetic and real data.

Contribution

It presents a novel recursive identification approach using latent variable modeling and convex majorization for nonlinear systems with multiple inputs and outputs.

Findings

Effective on synthetic nonlinear systems

Demonstrated on real-world nonlinear systems

Produces parsimonious predictive models

Abstract

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.