Identification of a weighted combination of multivariable state space systems from input and output data

V. Verdult, M.H.G. Verhaegen

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    Discusses a method for the determination of a weighted combination of local linear state-space systems from input and output data. The method is iterative and each iteration consists of two steps. The first step is to determine the weighting functions given the local models. This problem is solved by using an extended Kalman smoother. The second step is to identify the local models given the weights. For this step we optimize a cost function that represents a tradeoff between local and global learning. For this optimization we use a gradient search method in combination with an appropriate projection in the parameter space to deal with similarity transformations
    Original languageUndefined
    Title of host publicationProceedings of the 40th IEEE Conference on Decision and Control
    Place of PublicationOrlando, Florida, USA
    Number of pages6
    ISBN (Print)0-7803-7063-5
    Publication statusPublished - 4 Dec 2001
    Event40th IEEE Conference on Decision and Control, CDC 2001 - Hyatt Regency Grand Cypress, Orlando, United States
    Duration: 4 Dec 20017 Dec 2001

    Publication series

    PublisherIEEE Control Systems Society


    Conference40th IEEE Conference on Decision and Control, CDC 2001
    Abbreviated titleCDC
    Country/TerritoryUnited States


    • METIS-205354
    • iterative methods
    • Smoothing methods
    • covariance matrices
    • Kalman filters
    • Jacobian matrices
    • State estimation
    • multivariable systems
    • State-spacemethods
    • Parameter estimation
    • nonlinear filters
    • Linear systems
    • IR-37644

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