Support-Vector-based Least Squares for learning non-linear dynamics

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    Abstract

    A function approximator is introduced that is based on least squares support vector machines (LSSVM) and on least squares (LS). The potential indicators for the LS method are chosen as the kernel functions of all the training samples similar to LSSVM. By selecting these as indicator functions the indicators for LS can be interpret in a support vector machine setting and the curse of dimensionality can be circumvented. The indicators are included by a forward selection scheme. This makes the computational load for the training phase small. As long as the function is not approximated good enough, and the function is not overfitting the data, a new indicator is included. To test the approximator the inverse nonlinear dynamics of a linear motor are learnt. This is done by including the approximator as learning mechanism in a learning feedforward controller.
    Original languageEnglish
    Title of host publicationProceedings of the 41st IEEE Conference on Decision and Control
    Place of PublicationLas Vegas, NV
    PublisherIEEE
    Pages1343-1348
    Number of pages6
    Volume2
    ISBN (Print)0-7803-7517-3, 0-7803-7516-5
    DOIs
    Publication statusPublished - 10 Dec 2002
    Event41st IEEE Conference on Decision and Control, CDC 2002 - Las Vegas, United States
    Duration: 10 Dec 200213 Dec 2002
    Conference number: 41

    Publication series

    NameProceedings IEEE Conference on Decision and Control
    PublisherIEEE
    Volume2002
    ISSN (Print)0191-2216

    Conference

    Conference41st IEEE Conference on Decision and Control, CDC 2002
    Abbreviated titleCDC
    CountryUnited States
    CityLas Vegas
    Period10/12/0213/12/02

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