Nonlinear System Identification using Neural Networks and Trajectory-based Optimization

Hamid Khodabandehlou, M. Fadali

2019

Abstract

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.

Paper Citation

in Harvard Style

Khodabandehlou H. and Fadali M. (2019). Nonlinear System Identification using Neural Networks and Trajectory-based Optimization.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 579-586. DOI: 10.5220/0007772605790586

in Bibtex Style

@conference{icinco19,
author={Hamid Khodabandehlou and M. Fadali},
title={Nonlinear System Identification using Neural Networks and Trajectory-based Optimization},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={579-586},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007772605790586},
isbn={978-989-758-380-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Nonlinear System Identification using Neural Networks and Trajectory-based Optimization
SN - 978-989-758-380-3
AU - Khodabandehlou H.
AU - Fadali M.
PY - 2019
SP - 579
EP - 586
DO - 10.5220/0007772605790586