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- W2914473478 abstract "We address the problem of learning the parameters of a stable linear time invariant (LTI) system with unknown latent space dimension, or textit{order}, from its noisy input-output data. In particular, we focus on learning the parameters of the best lower order approximation allowed by the finite data. This is achieved by constructing a Hankel-like representation of the underlying system using ordinary least squares. Such a representation circumvents the non-convexities that typically arise in system identification, and it allows accurate estimation of the underlying LTI system. Our results rely on a careful analysis of a self-normalized martingale difference term that helps bound identification error up to logarithmic factors of the lower bound. We provide a data-dependent scheme for order selection and find a realization of system parameters, corresponding to that order, by an approach that is closely related to the celebrated Kalman-Ho subspace algorithm. We show that this realization is a good approximation of the underlying LTI system with high probability. Finally, we demonstrate that the proposed model order selection procedure is minimax optimal, i.e., for the given data length it is not always possible to estimate higher order models or find higher order approximations with reasonable accuracy." @default.
- W2914473478 created "2019-02-21" @default.
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- W2914473478 date "2019-02-05" @default.
- W2914473478 modified "2023-09-25" @default.
- W2914473478 title "Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order." @default.
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