An adaptive dynamic evolution feedforward neural network on modified particle swarm optimization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
High-order hopfield-based neural network for nonlinear system identification
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.