Nonparametric System identification of Stochastic Switched Linear Systems
Author(s)Sarkar, Tuhin; Rakhlin, Alexander; Dahleh, Munther A
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We address the problem of learning the parameters of a mean square stable switched linear systems(SLS) with unknown latent space dimension, or order, from its noisy input-output data. In particular, we focus on learning a good lower order approximation of the underlying model allowed by finite data. This is achieved by constructing Hankel-like matrices from data and obtaining suitable approximations via SVD truncation where the threshold for SVD truncation is purely data dependent. By exploiting tools from theory of model reduction for SLS, we find that the system parameter estimates are close to a balanced truncated realization of the underlying system with high probability.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Proceedings of the IEEE Conference on Decision and Control
Institute of Electrical and Electronics Engineers (IEEE)
Sarkar, Tuhin et al. “Nonparametric System identification of Stochastic Switched Linear Systems.” Paper in the Proceedings of the IEEE Conference on Decision and Control, 2019-December, 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, 11-13 Dec. 2019, IEEE © 2019 The Author(s)