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

    45 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
    Pages123-130
    Number of pages8
    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)0191-2216

    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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