Chapter
Apr 26, 2012
The Prediction of Derailment Coefficient Using NARX Neural Network
Authors: Xue-Miao Pang [email protected], Yong Qin, Zong-Yi Xing [email protected], and Li-Min JiaAuthor Affiliations
Publication: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation
Abstract
In railway system, track inspection vehicles regularly measure the vertical and lateral irregularities of each rail in order to ensure the safe running of railway. Derailment coefficient is an important indicator of the vehicle safety when the vertical wheel-rail force and lateral wheel-rail force are integrated together. In this paper, NARX neural network is proposed to predict derailment coefficient by measuring the track irregularities. In order to improve the generalization of the neural networks, the Bayesian Regularization algorithm is employed to train the neural networks. The experiments are carried out and the results show that the NARX neural network with Bayesian Regularization algorithm can predict derailment coefficient accurately.
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© 2011 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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School of mechanical engineering, Nanjing University of Science and Technology, 210094, China.E-mail: [email protected]
Yong Qin
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, China
School of mechanical engineering, Nanjing University of Science and Technology, 210094, China.E-mail: [email protected]
Li-Min Jia
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, China
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