Nonparametric System identification of Stochastic Switched Linear Systems
Name
1909.04617v1.pdf
Description
Submitted version
Size
200.94 KB
Format
Adobe PDF
Checksum (MD5)
5de071558b76e11c0bd20f28b5301fad
Author(s)
Sarkar, Tuhin
Rakhlin, Alexander
Dahleh, Munther A
Date Issued
March 2020
Journal
Proceedings of the IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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)
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC40024.2019.9030173
