Feed-Forward Neural Networks with Trainable Delay
Abstract
In this paper we build a bridge between feed-forward neural networks and delayed dynamical systems. As an initial demonstration, we capture the car-following behavior of a connected automated vehicle that includes time delay by using both simulation data and experimental data. We construct a delayed feed-forward neural network (DFNN) and introduce a training algorithm in order to learn the delay. We demonstrate that this algorithm works well on the proposed structures.
Cite
Text
Ji et al. "Feed-Forward Neural Networks with Trainable Delay." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Ji et al. "Feed-Forward Neural Networks with Trainable Delay." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/ji2020l4dc-feedforward/)BibTeX
@inproceedings{ji2020l4dc-feedforward,
title = {{Feed-Forward Neural Networks with Trainable Delay}},
author = {Ji, Xunbi A. and Molnár, Tamás G. and Avedisov, Sergei S. and Orosz, Gábor},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {127-136},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/ji2020l4dc-feedforward/}
}