View source for Linear System Identifiability from Distributional and Time Series Data

{{HTDB paper
| authors = Anandh Swaminathan and Richard M. Murray
| title = Linear System Identifiability from Distributional and Time Series Data
| source = 2016 American Control Conference (ACC)
| year = 2015
| type = Conference Paper
| funding = AFOSR BRI
| url = http://www.cds.caltech.edu/~murray/preprints/sm16-acc.pdf
| abstract = 
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.
| flags = 
| filetype = PDF
| filesize = 377K
| tag = sm16-acc
| id = 2015h
}}