Nonlinear System Identification using Neural Networks and Trajectory-Based Optimization

Autor:	Khodabandehlou, Hamid, Fadali, Mohammed Sami
Rok vydání:	2018
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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 methodology, 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. Comment: This paper is based upon work supported by the National Science Foundation under Grant No. IIA-1301726
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1804.10346 Zobrazit plný text záznamu View this record from Arxiv