Feed-forward Neural Networks with Trainable Delay

被引：0

作者：

Ji, Xunbi A. ^{[1
]}

Molnar, Tamas G. ^{[1
]}

Avedisov, Sergei S. ^{[1
,2
]}

Orosz, Gabor ^{[1
]}

机构：

[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA

[2] Toyota Motor North Amer R&D Infotech Labs, Mountain View, CA 94043 USA

来源：

LEARNING FOR DYNAMICS AND CONTROL, VOL 120 | 2020年 / 120卷

关键词：

delayed feed-forward neural network; car-following; connected automated vehicle; time delay system;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：127 / 136

页数：10

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