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arXiv · 1710.04009

Regularized parametric system identification: a decision-theoretic formulation

Johan Wågberg·Dave Zachariah·Thomas B. Schön
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Abstract

Parametric prediction error methods constitute a classical approach to the identification of linear dynamic systems with excellent large-sample properties. A more recent regularized approach, inspired by machine learning and Bayesian methods, has also gained attention. Methods based on this approach estimate the system impulse response with excellent small-sample properties. In several applications, however, it is desirable to obtain a compact representation of the system in the form of a parametric model. By viewing the identification of such models as a decision, we develop a decision-theoretic formulation of the parametric system identification problem that bridges the gap between the classical and regularized approaches above. Using the output-error model class as an illustration, we show that this decision-theoretic approach leads to a regularized method that is robust to small sample-sizes as well as overparameterization.

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Johan Wågberg, Dave Zachariah, Thomas B. Schön. 2017-10-11. Regularized parametric system identification: a decision-theoretic formulation. https://arxiv.org/abs/1710.04009

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