A convex optimization approach to model (in)validation of switched ARX systems with unknown switches

Y. Cheng; Y. Wang; M. Sznaier; N. Ozay; C. Lagoa

doi:10.1109/CDC.2012.6426518

A convex optimization approach to model (in)validation of switched ARX systems with unknown switches

Y. Cheng, Y. Wang, M. Sznaier, N. Ozay, C. Lagoa

Research output: Contribution to journal › Conference article

9 Scopus citations

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.

Original language	English (US)
Article number	6426518
Pages (from-to)	6284-6290
Number of pages	7
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/CDC.2012.6426518
State	Published - Dec 1 2012
Event	51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States Duration: Dec 10 2012 → Dec 13 2012

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC.2012.6426518

Fingerprint Dive into the research topics of 'A convex optimization approach to model (in)validation of switched ARX systems with unknown switches'. Together they form a unique fingerprint.

Cite this

Cheng, Y., Wang, Y., Sznaier, M., Ozay, N., & Lagoa, C. (2012). A convex optimization approach to model (in)validation of switched ARX systems with unknown switches. Proceedings of the IEEE Conference on Decision and Control, 6284-6290. [6426518]. https://doi.org/10.1109/CDC.2012.6426518

@article{463cbc538db544819afe84cc5dfa6a58,

title = "A convex optimization approach to model (in)validation of switched ARX systems with unknown switches",

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.",

author = "Y. Cheng and Y. Wang and M. Sznaier and N. Ozay and C. Lagoa",

year = "2012",

month = dec,

day = "1",

doi = "10.1109/CDC.2012.6426518",

language = "English (US)",

pages = "6284--6290",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0191-2216",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "51st IEEE Conference on Decision and Control, CDC 2012 ; Conference date: 10-12-2012 Through 13-12-2012",

}

TY - JOUR

T1 - A convex optimization approach to model (in)validation of switched ARX systems with unknown switches

AU - Cheng, Y.

AU - Wang, Y.

AU - Sznaier, M.

AU - Ozay, N.

AU - Lagoa, C.

PY - 2012/12/1

Y1 - 2012/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84874257589&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874257589&partnerID=8YFLogxK

U2 - 10.1109/CDC.2012.6426518

DO - 10.1109/CDC.2012.6426518

M3 - Conference article

AN - SCOPUS:84874257589

SP - 6284

EP - 6290

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

SN - 0191-2216

M1 - 6426518

T2 - 51st IEEE Conference on Decision and Control, CDC 2012

Y2 - 10 December 2012 through 13 December 2012

ER -