An Efficient Parameterization of Dynamic Neural Networks for Nonlinear System IdentificationBecerra, V.M., Garces, F., Nasuto, S.J. and Holderbaum, W. ORCID: https://orcid.org/0000-0002-1677-9624 (2005) An Efficient Parameterization of Dynamic Neural Networks for Nonlinear System Identification. IEEE Transactions on Neural Networks, 16 (4). 983 - 988. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/TNN.2005.849844 Abstract/SummaryDynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.
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