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 › peer-review

10 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 - 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

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10.1109/CDC.2012.6426518

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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",

doi = "10.1109/CDC.2012.6426518",

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

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AU - Cheng, Y.

AU - Wang, Y.

AU - Sznaier, M.

AU - Ozay, N.

AU - Lagoa, C.

PY - 2012

Y1 - 2012

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.

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JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

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

Y2 - 10 December 2012 through 13 December 2012

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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