Feed-forward Neural Networks with Trainable Delay

Xunbi A. Ji, Tamás G. Molnár, Sergei S. Avedisov, Gábor Orosz
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:127-136, 2020.

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 this Paper

BibTeX
@InProceedings{pmlr-v120-ji20a, title = {Feed-forward Neural Networks with Trainable Delay}, author = {Ji, Xunbi A. and Moln\'ar, Tam\'as G. and Avedisov, Sergei S. and Orosz, G\'abor}, booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control}, pages = {127--136}, year = {2020}, editor = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie}, volume = {120}, series = {Proceedings of Machine Learning Research}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/ji20a/ji20a.pdf}, url = {https://proceedings.mlr.press/v120/ji20a.html}, 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.} }
Endnote
%0 Conference Paper %T Feed-forward Neural Networks with Trainable Delay %A Xunbi A. Ji %A Tamás G. Molnár %A Sergei S. Avedisov %A Gábor Orosz %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-ji20a %I PMLR %P 127--136 %U https://proceedings.mlr.press/v120/ji20a.html %V 120 %X 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.
APA
Ji, X.A., Molnár, T.G., Avedisov, S.S. & Orosz, G.. (2020). Feed-forward Neural Networks with Trainable Delay. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 120:127-136 Available from https://proceedings.mlr.press/v120/ji20a.html.

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