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- W4386874976 abstract "Identifying a linear system model from data has wide applications in control theory. The existing work on finite sample analysis for linear system identification typically uses data from a single system trajectory under i.i.d random inputs, and assumes that the underlying dynamics is truly linear. In contrast, we consider the problem of identifying a linearized model when the true underlying dynamics is nonlinear. We provide a multiple trajectories-based deterministic data acquisition algorithm followed by a regularized least squares algorithm, and provide a finite sample error bound on the learned linearized dynamics. Our error bound demonstrates a trade-off between the error due to nonlinearity and the error due to noise, and shows that one can learn the linearized dynamics with arbitrarily small error given sufficiently many samples. We validate our results through experiments, where we also show the potential insufficiency of linear system identification using a single trajectory with i.i.d random inputs, when nonlinearity does exist." @default.
- W4386874976 created "2023-09-20" @default.
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- W4386874976 date "2023-09-15" @default.
- W4386874976 modified "2023-09-27" @default.
- W4386874976 title "Learning Linearized Models from Nonlinear Systems with Finite Data" @default.
- W4386874976 doi "https://doi.org/10.48550/arxiv.2309.08805" @default.
- W4386874976 hasPublicationYear "2023" @default.
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