Nonlinear System Identification using Neural Networks and Trajectory-based Optimization

Hamid Khodabandehlou, M. Fadali
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
more » ... ology, uses two different nonlinear dynamical systems to find the connected components of the feasible region and then searches the regions for local minima of the optimization problem. Simulation results show that both approaches effectively identify the model of the cascade tanks and the Bouc-Wen model. Khodabandehlou, H. and Fadali, M. Nonlinear System Identification using Neural Networks and Trajectory-based Optimization.
doi:10.5220/0007772605790586 dblp:conf/icinco/KhodabandehlouF19 fatcat:vaofx7gepfemzfnekikazkjbr4