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Intelligent localization of a high-speed train using lssvm and the online sparse optimization approach
Cheng R.1; Song Y.1; Chen D.3; Chen L.2
2017-08-01
Source PublicationIEEE Transactions on Intelligent Transportation Systems
ISSN15249050
Volume18Issue:8Pages:2071-2084
Abstract

For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L-norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L-norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai high-speed railway (BS-HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS-HSR illustrate that these methods achieve sparse models and increase the real-time performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.

KeywordHigh-speed Train Iterative Pruning Error Minimization l?L?-norm Minimization Location Error Lssvm Online Sparse Optimization
DOI10.1109/TITS.2016.2633344
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000407347300006
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被引频次[WOS]:7   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Beijing Jiaotong University
2.Universidade de Macau
3.Fuzhou University
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Cheng R.,Song Y.,Chen D.,et al. Intelligent localization of a high-speed train using lssvm and the online sparse optimization approach[J]. IEEE Transactions on Intelligent Transportation Systems,2017,18(8):2071-2084.
APA Cheng R.,Song Y.,Chen D.,&Chen L..(2017).Intelligent localization of a high-speed train using lssvm and the online sparse optimization approach.IEEE Transactions on Intelligent Transportation Systems,18(8),2071-2084.
MLA Cheng R.,et al."Intelligent localization of a high-speed train using lssvm and the online sparse optimization approach".IEEE Transactions on Intelligent Transportation Systems 18.8(2017):2071-2084.
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