Uncertainty quantification of data-driven output predictors in the output error setting
Kaviani, Farzan
Kaviani, Farzan
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Abstract
We study output prediction for LTI systems using only input-output data, without identifying a parametric model. Prior methods rely on projecting recent signal data onto a Hankel matrix built from offline data, but this becomes inaccurate in the presence of noise. While low-rank approximations like truncated SVD are used to mitigate noise, their effect on prediction accuracy remains unclear. We derive two upper bounds on the prediction error under small noise assumptions—one using raw data, the other with low-rank approximations. Our bounds require only noisy measurements and known system order. Simulations confirm the bounds’ linear decay with noise level.
Description
Graduate
Date
2025-06-02