title: An efficient parameterization of dynamic neural networks for nonlinear system identification.
abstract: Dynamic 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.
authors.forename: Victor M
authors.initials: VM
authors.lastname: Becerra
authors.forename: Freddy R
authors.initials: FR
authors.lastname: Garces
authors.forename: Slawomir J
authors.initials: SJ
authors.lastname: Nasuto
authors.forename: William
authors.initials: W
authors.lastname: Holderbaum
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*