NDLI: Structure and weight initialization of feed-forward networks for traffic flow-density relationships

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Structure and weight initialization of feed-forward networks for traffic flow-density relationships

Content Provider	IEEE Xplore Digital Library
Author	Messai, N. Thomas, P. Lefebvre, D. El Moudni, A.
Copyright Year	2001
Description	Author affiliation: UTBM, Belfort, France (Messai, N.)
Abstract	Urban traffic is a complex process that is often described by macroscopic flow models. On one hand, this work justified how feedforward networks (NN) with two neurons in the hidden layer can fit real traffic data. On the other hand, two novel initializations are suggested. Both methods exploit the NN traffic model structure and the infrastructure parameters to ensure that the outputs of neurons are in the active region and speed up the learning convergence.
Sponsorship	Int. Neural Network Soc.
Starting Page	2059
Ending Page	2064
File Size	472947
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938483
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feedforward systems Telecommunication traffic Traffic control Neural networks Neurons Microscopy Vehicles Electronic mail Convergence Parameter estimation
Content Type	Text
Resource Type	Article

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