A variant of the stochastic fundamental lemma for LTI systems that enables trajectory prediction without past disturbance data in Hankel matrices via polynomial chaos expansions and known disturbance distributions.
& author D \"o rfler, F
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Finite-sample signal subspaces extend the fundamental lemma to enable a projection-based data-driven fault detection method with performance analysis via matrix perturbation theory.
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A Stochastic Fundamental Lemma with Reduced Disturbance Data Requirements
A variant of the stochastic fundamental lemma for LTI systems that enables trajectory prediction without past disturbance data in Hankel matrices via polynomial chaos expansions and known disturbance distributions.
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System representations in subspaces of finite-sample signals and their application to data-driven fault detection
Finite-sample signal subspaces extend the fundamental lemma to enable a projection-based data-driven fault detection method with performance analysis via matrix perturbation theory.