Intelligent Localization of a High-Speed Train Using LSSVM and the Online Sparse Optimization Approach

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...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 18; H. 8; S. 2071 - 2084
Hauptverfasser:	Cheng, Ruijun, Song, Yongduan, Chen, Dewang, Chen, Long
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Computational modeling Computer memory Data models Distance learning High speed High speed rail High-speed train Iterative methods iterative pruning error minimization Localization location error LSSVM L₀-norm minimization Machine learning Mathematical model online sparse optimization Optimization Pruning Rail transportation Real time Real-time systems Support vector machines
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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