Near optimal finite time identification of arbitrary linear dynamical systems
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:56105618, 2019.
Abstract
We derive finite time error bounds for estimating general linear timeinvariant (LTI) systems from a single observed trajectory using the method of least squares. We provide the first analysis of the general case when eigenvalues of the LTI system are arbitrarily distributed in three regimes: stable, marginally stable, and explosive. Our analysis yields sharp upper bounds for each of these cases separately. We observe that although the underlying process behaves quite differently in each of these three regimes, the systematic analysis of a self–normalized martingale difference term helps bound identification error up to logarithmic factors of the lower bound. On the other hand, we demonstrate that the least squares solution may be statistically inconsistent under certain conditions even when the signaltonoise ratio is high.
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