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Nonlinear System Identification using Neural Networks and Trajectory-based Optimization
2019
Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
In this paper, we study the identification of two challenging benchmark problems using neural networks. Two different global optimization approaches are used to train a recurrent neural network to identify two challenging nonlinear models, the cascaded tanks and the Bouc-Wen system. The first approach, quotient gradient system (QGS), uses the trajectories of the nonlinear dynamical system to find the local minima of the optimization problem. The second approach, dynamical trajectory based
doi:10.5220/0007772605790586
dblp:conf/icinco/KhodabandehlouF19
fatcat:vaofx7gepfemzfnekikazkjbr4