A novel dynamic neural network structure for nonlinear system identification

Jiamei Deng, Victor M. Becerra, Slawomir J. Nasuto

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


    Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained to identify a practical nonlinear 3D crane system.
    Original languageEnglish
    Title of host publicationProceedings of the 16th IFAC World Congress, 2005
    PublisherInternational Federation of Automatic Control (IFAC)
    ISBN (Print)9783902661753
    Publication statusPublished - 2005

    Publication series

    NameWorld congress


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