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Linear System Identifiability from Distributional and Time Series Data

Swaminathan, Anandh and Murray, Richard M. (2016) Linear System Identifiability from Distributional and Time Series Data. In: 2016 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 392-399. ISBN 978-1-4673-8680-7.

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We consider identifiability of linear systems driven by white noise using a combination of distributional and time series measurements. Specifically, we assume that the system has no control inputs available and can only be observed at stationarity. The user is able to measure the full stationary state distribution as well as observe time correlations for small subsets of the state. We formulate theoretical conditions on identifiability of parameters from distributional information alone. We then give a sufficient condition and an effective necessary condition for identifiability using a combination of distributional and time series measurements. We illustrate the ideas with some simple examples as well as a biologically inspired example of a transcription and degradation process.

Item Type:Book Section
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URLURL TypeDescription
Swaminathan, Anandh0000-0001-9935-6530
Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2016 AACC. This work was supported by AFOSR grant FA9550-14-1-0060. A. Swaminathan was also supported by the NSF GRFP. We acknowledge Professor Eduardo Sontag for helpful advice. We especially acknowledge Dr. Yutaka Hori for helpful advice as well as detailed comments on the manuscript. We also thank Reviewer 3 for very detailed feedback.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-14-1-0060
NSF Graduate Research FellowshipUNSPECIFIED
Record Number:CaltechAUTHORS:20160802-094940806
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Official Citation:A. Swaminathan and R. M. Murray, "Linear system identifiability from distributional and time series data," 2016 American Control Conference (ACC), Boston, MA, USA, 2016, pp. 392-399. doi: 10.1109/ACC.2016.7524946
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:69381
Deposited By: Ruth Sustaita
Deposited On:02 Aug 2016 23:27
Last Modified:09 Mar 2020 13:19

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