[1] GUO C, YANG Y, PAN H, et al. Fault analysis of high speed train with DBN hierarchical ensembles[C]// Proceedings of the International Joint Conference on Neural Networks. Vancouver:IEEE Press, 2016: 2552-2559. [2] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]// Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. London: The Royal Society, 1998, 454(1971): 903-995. [3] 李智敏, 苟先太, 秦娜, 等. 高速列车振动监测信号的频率特征[J]. 仪表技术与传感器, 2015, (5): 99-103.
[4] ZHAO J, YANG Y, LI T, et al. Application of Empirical Mode Decomposition and Fuzzy Entropy to High-Speed Rail Fault Diagnosis[M]. Foundations of Intelligent Systems. Springer Berlin Heidelberg, 2014: 93-103. [5] 刘林艳, 金炜东, 余志斌. 基于小波分析的高速列车车体运行状态估计[J]. 计算机应用研究, 2013, 30(10): 2948-2950. [6] XU C, TAO D, XU C. A survey on multi-view learning[EB/OL]. [2017-05-10] https://arxiv.org/pdf/1304.5634v1.pdf. [7] ROKACH L. A survey of clustering algorithms[J]. Data Mining and Knowledge Discovery Handbook, 2009, 16(3): 269-298. [8] STREHL A, GHOSH J. Cluster ensembles: A knowledge reuse framework for combining partitionings[C]// Proceedings of the 8th National Conference on Artificial Intelligence. Edmonton, Canada: ACM Press, 2002: 93-99. [9] GREENE D, CUNNINGHAM P. A matrix factorization approach for integrating multiple data views[C]// Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2009: 423-438. [10] 黄采伦, 樊晓平, 陈春阳, 等. 基于小波系数提取及离散余弦包络分析的机车牵引齿轮故障诊断方法[J]. 铁道学报, 2008, 30(2): 98-102. [11] 张乾. 基于振动信号的轴承状态监测和故障诊断方法研究[D]. 长沙: 中南大学, 2012. [12] SRA S, DHILLON I S. Nonnegative matrix approximation: Algorithms and applications[EB/OL]. [2017-04-28]http://www.cs.utexas.edu/ftp/techreports/tr06-27.pdf. [13] 李乐, 章毓晋. 非负矩阵分解算法综述[J]. 电子学报, 2008, 36(4): 737-743. [14] 杨燕, 靳蕃, KAMEL M. 聚类有效性评价综述[J]. 计算机应用研究, 2008, 25(6): 1630-1632. [15] CAI X, NIE F, HUANG H. Multi-view k-means clustering on big data[C]// Proceedings of the 23th International Joint Conference on Artificial Intelligence. Beijing: IEEE Press2013: 2598-2604. [16] XIA R, PAN Y, DU L, et al. Robust multi-view spectral clustering via low-rank and sparse decomposition[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: ACM Press, 2014: 2149-2155. [17] KUMAR A, RAI P, DAUMé H. Co-regularized multi-view spectral clustering[C]// Proceedings of the 24th International Conference on Neural Information Processing Systems. Granada, Spain: ACM Press, 2011: 1413-1421.
() ( |