The combination of Kalman filtering technology and artificial neural networks technology has provided a new method for forecasting the changes of track geometry state. This article has established a forecasting model for the track geometry state of BP neural network based on Kalman filtering. The author analyzed some track detecting data of Chengdu-Kunming railway line of Kunming Railway Bureau, and made the state forecast based on this model. Simulation result showed that the application of BP neural network algorithm based on Kalman filtering in forecasting rail state has very high accuracy. The experiment showed that the application of this technology could forecast changes of the track geometry state in advance, and could make in-time adjustment to the track maintenance service work. Thus it is an effective measure for maintaining track geometry state in the best condition.
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2010 Joint Rail Conference
April 27–29, 2010
Urbana, Illinois, USA
Conference Sponsors:
- Rail Transportation Division
ISBN:
978-0-7918-4906-4
PROCEEDINGS PAPER
Forecasting of the Track State Using BP Neural Network Based on Kalman Filtering
Chaolong Jia,
Chaolong Jia
Beijing JiaoTong University, Beijing, China
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Weixiang Xu,
Weixiang Xu
Beijing JiaoTong University, Beijing, China
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Xumin Liu
Xumin Liu
Capital Normal University, Beijing, China
Search for other works by this author on:
Chaolong Jia
Beijing JiaoTong University, Beijing, China
Weixiang Xu
Beijing JiaoTong University, Beijing, China
Xumin Liu
Capital Normal University, Beijing, China
Paper No:
JRC2010-36163, pp. 293-298; 6 pages
Published Online:
October 28, 2010
Citation
Jia, C, Xu, W, & Liu, X. "Forecasting of the Track State Using BP Neural Network Based on Kalman Filtering." Proceedings of the 2010 Joint Rail Conference. 2010 Joint Rail Conference, Volume 1. Urbana, Illinois, USA. April 27–29, 2010. pp. 293-298. ASME. https://doi.org/10.1115/JRC2010-36163
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