[1] |
孙上鹏. 无绝缘轨道电路故障诊断方法研究[D]. 北京: 北京交通大学, 2014.
|
|
SUN Shangpeng. Research on fault diagnosis for railway jointless track circuits[D]. Beijing: Beijing Jiaotong University, 2014.
|
[2] |
BRUIN T D, VERBERT K, ROBERT B. Railway track circuit fault diagnosis using recurrent neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(3):523-533.
doi: 10.1109/TNNLS.2016.2551940
|
[3] |
朱文博, 王小敏. 基于组合决策树的无绝缘轨道电路故障诊断方法研究[J]. 铁道学报, 2018, 40(7):79-84.
|
|
ZHU Wenbo, WANG Xiaomin. Research on fault diagnosis of railway jointless track circuit based on combinatorial decision tree[J]. Journal of the China Railway Society, 2018, 40(7):79-84.
|
[4] |
王梓丞, 张亚东, 郭进, 等. 基于区间二型神经模糊系统的轨道电路故障诊断[J]. 西南交通大学学报, 2021, 56(1):190-196.
|
|
WANG Zicheng, ZHANG Yadong, GUO Jin, et al. Fault diagnosis for track circuit based on interval type-2 neural-fuzzy system[J]. Journal of Southwest Jiaotong University, 2021, 56(1):190-196.
|
[5] |
GALAR M, FERNANDEZ A, BARRENECHEA E, et al. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches[J]. IEEE Transactions on Systems Man & Cybernetics Part C, 2012, 42(4):463-484.
|
[6] |
邵良杉, 李相辰. 不平衡数据下的采空区自然发火预测研究[J]. 中国安全科学学报, 2017, 27(6): 61-66.
doi: 10.16265/j.cnki.issn1003-3033.2017.06.011
|
|
SHAO Liangshan, LI Xiangchen. Prediction of coal spontaneous combustion in caving zone with unbalanced data[J]. China Safety Science Journal, 2017, 27(6): 61-66.
doi: 10.16265/j.cnki.issn1003-3033.2017.06.011
|
[7] |
邵良杉, 周玉. 基于PSO-ELM-Boosting模型的底板破坏深度预测[J]. 中国安全科学学报, 2018, 28(4): 24-29.
doi: 10.16265/j.cnki.issn1003-3033.2018.04.005
|
|
SHAO Liangshan, ZHOU Yu. Prediction of destroyed floor depth based on SO-ELM-Boosting model[J]. China Safety Science Journal, 2018, 28(4): 24-29.
doi: 10.16265/j.cnki.issn1003-3033.2018.04.005
|
[8] |
YOAV F, ROBERT E S. A decision theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1):119-139.
doi: 10.1006/jcss.1997.1504
|
[9] |
LIAW A, WIENER M. Classification and regression by randomforest[J]. R News, 2002, 2/3:18-22.
|
[10] |
FRIEDMAN J H. Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 2001, 29(5):1189-1232.
doi: 10.1214/aos/1013203450
|
[11] |
CHEN Tianqi, CARLOS G. XGBoost: a scalable tree boosting system[C]. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 785-794.
|
[12] |
KE Guolin, MENG Q, FINLEY T, et al. Lightgbm: a highly efficient gradient boosting decision tree[C]. Advances in Neural Information Processing Systems, 2017: 3146-3154.
|