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Proceedings Paper

Application of wavelet neural network model based on genetic algorithm in the prediction of high-speed railway settlement
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Paper Abstract

With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods’. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.

Paper Details

Date Published: 9 December 2015
PDF: 7 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082P (9 December 2015); doi: 10.1117/12.2222200
Show Author Affiliations
Shihua Tang, Guilin Univ. of Technology (China)
Feida Li, Guilin Univ. of Technology (China)
Yintao Liu, Guilin Univ. of Technology (China)
Lan Lan, Guilin Univ. of Technology (China)
Conglin Zhou, Guilin Univ. of Technology (China)
Qing Huang, Guilin Univ. of Technology (China)

Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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