@inproceedings{f19ff8c77e774cd2a5d9997a8466a4b4,
title = "Dynamic modeling of robot based on neural network with incomplete state observations",
abstract = "This paper presents a novel dynamic modeling method of robot system using a recurrent neural network (RNN) with incomplete state variables observation. A dynamic model of a 2-DOF articulated robot is discussed, and the corresponding training method is deduced based on the back propagation through time (BPTT) algorithm. The effectiveness of this process is verified by simulation. The results show that the observed state variables are regressed, and the unobserved state variables are estimated.",
keywords = "BPTT, Dynamic modeling, Incomplete state RNN, State feedback",
author = "Changjun Li and Fei Zhao and Tao Tao and Xuesong Mei",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 ; Conference date: 05-12-2017 Through 08-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ROBIO.2017.8324765",
language = "英语",
series = "2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2320--2324",
booktitle = "2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017",
}