NDLI: Application of artificial neural networks for path loss prediction in railway environments

Content Provider	IEEE Xplore Digital Library
Author	Di Wu Gang Zhu Ai, B.
Copyright Year	2010
Description	Author affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, China (Di Wu; Gang Zhu; Ai, B.)
Abstract	To balance the precision and generality of the prediction model, a new path loss artificial neural network (ANN) prediction model for railway environments is presented firstly in this paper. The utilization of back propagation ANN can overcome some disadvantages of such conventional prediction models as empirical and deterministic models. The training data is based on the electric field strength measurements in the Zhengzhou-Xi'an express railway line in China. Through many attempts and comparisons, the suitable architecture and learning algorithm are chosen in the proposed model. After training, the proposed model can predict the path losses accurately in typical similar railway environments. Comparisons between a conventional model and the proposed model, with the measured and predicted data show that the proposed model is sufficiently applicable in railway scenarios.
Starting Page	1
Ending Page	5
File Size	894615
Page Count	5
File Format	PDF
e-ISBN	9780984589333
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	back propagation network (BPN) Computer languages learning algorithm Artificial neural networks Predictive models Data models Artificial neural network (ANN) railway Mathematical model path loss
Content Type	Text
Resource Type	Article

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