Model (in)validation of switched ARX systems with unknown switches and its application to activity monitoring

Necmiye Ozay, Mario Sznaier, Constantino Lagoa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    16 Scopus citations


    Identification of switched linear systems has received considerable attention during the past few years. Since the problem is generically NP-Hard, the majority of existing algorithms are based on heuristics or relaxations. Therefore, it is crucial to check the validity of the identified models against additional experimental data. This paper addresses the problem of model (in)validation for multi-input multioutput switched affine autoregressive exogenous systems with unknown switches. Our main result provides necessary and sufficient conditions for a given model to be (in)validated by the experimental data. In principle, checking these conditions requires solving a sequence of convex optimization problems involving increasingly large matrices. However, as we show in the paper, if in the process of solving these problems either a positive solution is found or the so-called flat extension property holds, then the process terminates with a certificate that either the model has been invalidated or that the experimental data is indeed consistent with the model and a-priori information. By using duality, the proposed approach exploits the inherently sparse structure of the optimization problem to substantially reduce its computational complexity. The effectiveness of the proposed method is illustrated using both academic examples and a non-trivial problem arising in computer vision: activity monitoring.

    Original languageEnglish (US)
    Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages7
    ISBN (Print)9781424477456
    StatePublished - 2010
    Event49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States
    Duration: Dec 15 2010Dec 17 2010

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    ISSN (Print)0191-2216


    Conference49th IEEE Conference on Decision and Control, CDC 2010
    Country/TerritoryUnited States

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Modeling and Simulation
    • Control and Optimization


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