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- W3023502057 abstract "We address the problem of learning the parameters of a mean square stableswitched linear systems (SLS) with unknown latent space dimension, ortextit{order}, from its noisy input--output data. In particular, we focus onlearning a good lower order approximation of the underlying model allowed byfinite data. Motivated by subspace-based algorithms in system theory, weconstruct a Hankel-like matrix from finite noisy data using ordinary leastsquares. Such a formulation circumvents the non-convexities that arise insystem identification, and allows for accurate estimation of the underlying SLSas data size increases. Since the model order is unknown, the key idea of ourapproach is model order selection based on purely data dependent quantities. Weconstruct Hankel-like matrices from data of dimension obtained from the orderselection procedure. By exploiting tools from theory of model reduction forSLS, we obtain suitable approximations via singular value decomposition (SVD)and show that the system parameter estimates are close to a balanced truncatedrealization of the underlying system with high probability." @default.
- W3023502057 created "2020-05-13" @default.
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- W3023502057 date "2019-09-10" @default.
- W3023502057 modified "2023-09-25" @default.
- W3023502057 title "Data Driven Estimation of Stochastic Switched Linear Systems of Unknown Order" @default.
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