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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)
Pages1135
ISBN (Print)9783902661753
DOIs
Publication statusPublished - 2005

Publication series

NameWorld congress
PublisherIFAC
Number1
Volume16

Related information

Relations Get citation (various referencing formats)

ID: 3262580