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

共 50 条

[31] An efficient hardware implementation of feed-forward neural networks
Szabó, T
Horváth, G
APPLIED INTELLIGENCE, 2004, 21 (02) : 143 - 158
[32] A new scheme for training feed-forward neural networks
AbdelWahhab, O
SidAhmed, MA
PATTERN RECOGNITION, 1997, 30 (03) : 519 - 524
[33] A greenhouse control with feed-forward and recurrent neural networks
Fourati, Fathi
Chtourou, Mohamed
SIMULATION MODELLING PRACTICE AND THEORY, 2007, 15 (08) : 1016 - 1028
[34] Serial binary multiplication with feed-forward neural networks
Cotofana, S
Vassiliadis, S
NEUROCOMPUTING, 1999, 28 : 1 - 19
[35] Correlation of internal representations in feed-forward neural networks
Engel, A
JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 1996, 29 (13): : L323 - L327
[36] Optimizing and Learning Algorithm for Feed-forward Neural Networks
Bachiller, Pilar
González, Julia
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2001, 5 (01) : 51 - 57
[37] Prediction of attentional focus from respiration with simple feed-forward and time delay neural networks
Melnychuk, Michael Christopher
Murphy, Peter R.
Robertson, Ian H.
Balsters, Joshua H.
Dockree, Paul M.
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (18): : 14875 - 14884
[38] Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks
Zhai, Jianfeng
Zhou, Jianyi
Zhao, Jianing
Zhang, Lei
Hong, Wei
Journal of Southeast University (English Edition), 2008, 24 (01) : 6 - 9
[39] Prediction of attentional focus from respiration with simple feed-forward and time delay neural networks
Michael Christopher Melnychuk
Peter R. Murphy
Ian H. Robertson
Joshua H. Balsters
Paul M. Dockree
Neural Computing and Applications, 2020, 32 : 14875 - 14884
[40] Distributed restoration in optical networks using feed-forward neural networks
Karpat, Demeter Gokisik
Bilgen, Semih
PHOTONIC NETWORK COMMUNICATIONS, 2006, 12 (01) : 53 - 64

← 1 2 3 4 5 →