Skip to content

A novel dynamic neural network structure for nonlinear system identification

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

Related information

Relations Get citation (various referencing formats)

ID: 3262580