A sparsification approach to set membership identification of a class of affine hybrid systems

Necmiye Ozay, Mario Sznaier, Constantino Lagoa, Octavia Camps

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

    43 Scopus citations

    Abstract

    This paper addresses the problem of robust identification of a class of discrete-time affine hybrid systems, switched affine models, in a set membership framework. Given a finite collection of noisy input/output data and some minimal a priori information about the set of admissible plants, the objective is to identify a suitable set of affine models along with a switching sequence that can explain the available experimental information, while optimizing a performance criteria (either minimum number of switches or minimum number of plants). Our main result shows that this problem can be reduced to a sparsification form, where the goal is to maximize sparsity of a given vector sequence. Although in principle this leads to an NP-hard problem, as we show in the paper, efficient convex relaxations can be obtained by exploiting recent results on sparse signal recovery. These results are illustrated using two non-trivial problems arising in computer vision applications: video-shot and dynamic texture segmentation.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages123-130
    Number of pages8
    ISBN (Print)9781424431243
    DOIs
    StatePublished - 2008
    Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
    Duration: Dec 9 2008Dec 11 2008

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    ISSN (Print)0743-1546
    ISSN (Electronic)2576-2370

    Other

    Other47th IEEE Conference on Decision and Control, CDC 2008
    Country/TerritoryMexico
    CityCancun
    Period12/9/0812/11/08

    All Science Journal Classification (ASJC) codes

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

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