Published December 2012
| public
Book Section - Chapter
A convex optimization approach to model (in)validation of switched ARX systems with unknown switches
- Creators
- Cheng, Y.
- Wang, Y.
- Sznaier, M.
- Ozay, N.
- Lagoa, C.
Abstract
This paper considers the problem of (in)validating switched affine models from noisy experimental data, in cases where the mode-variable is not directly observable. This problem, the dual of identification, is a crucial step when designing controllers using models identified from experimental data. Our main results are convex certificates, obtained by exploiting a combination of sparsification and polynomial optimization tools, for a given model to either be consistent with the observed data or be invalidated by it. These results are illustrated using both academic examples and a non-trivial application: detecting abnormal activities using video data.
Additional Information
© 2012 IEEE. This work was supported in part by NSF grant ECCS–0901433; AFOSR grant FA9559–12–1–0271; and DHS grant 2008-ST-061-ED0001.Additional details
- Eprint ID
- 74141
- Resolver ID
- CaltechAUTHORS:20170207-174716284
- ECCS–090143
- NSF
- FA9559–12–1–0271
- Air Force Office of Scientific Research (AFOSR)
- 2008-ST-061-ED0001
- Department of Homeland Security
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2017-02-08Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field