Page 77 - 《摩擦学学报》2021年第1期

74 摩 擦 学 学 报 第 41 卷

Budapest, 1998, 187196. Wear, 2018, 406: 173–184. doi: 10.1016/j.wear.2018.01.007.
[ 3 ] Jin Y, Ishida M, Namura A. Experimental simulation and prediction [14] Shi Xintian, Pang Jingyue, Zhang Xin, et al. Satellite big data
of wear of wheel flange and rail gauge corner[J]. Wear, 2011, 271(1- analysis based on integrated limit learning machine[J]. Journal of
2): 259–267. doi: 10.1016/j.wear.2010.10.032. Instrumentation, 2018, 39(12): 81–91 (in Chinese) [史欣田, 庞景月,
[ 4 ] Lin Fengtao. Research on wheel wear and wheel profile optimization 张新, 等. 基于集成极限学习机的卫星大数据分析[J]. 仪器仪表学
of high speed train[D]. China Academy of Railway Sciences, 报, 2018, 39(12): 81–91].
2014(in Chinese) [林凤涛. 高速列车车轮磨耗及型面优化研究[D]. [15] Xia Yanqiu, Xu Dayi, Feng Xin, et al. Type identification and
北京: 中国铁道科学研究院, 2014]. content prediction of lubricant additives based on limit learning
[ 5 ] Tao Gongquan, Li Xia, Deng Yongguo, et al. Wheel wear life machine and optimization algorithm[J]. Tribology, 2020, 40(1):
prediction based on vehicle lateral motion stability[J]. Journal of 97–106 (in Chinese) [夏延秋, 徐大祎, 冯欣, 等. 基于极限学习机
Mechanical Engineering, 2013, 49(10): 28–34 (in Chinese) [陶功权, 和优化算法的润滑油添加剂种类识别与含量预测[J]. 摩擦学学
李霞, 邓永果, 等. 基于车辆横向运动稳定性的车轮磨耗寿命预测 报, 2020, 40(1): 97–106]. doi: 10.16078/j.tribology.2019107.
[J]. 机械工程学报, 2013, 49(10): 28–34]. doi: 10.3901/JME.2013.10. [16] Niu Peifeng, Li Jinbai, Liu Nan, et al. NO x emission optimization of
028. boiler based on improved flower pollination algorithm and limit
[ 6 ] Du Wei. Analysis of factors affecting wheel rail wear in curve learning machine[J]. Journal of Power Engineering, 2018, 38(10):
section of heavy haul railway[D]. Chengdu: Southwest Jiaotong 782–787 (in Chinese) [牛培峰, 李进柏, 刘楠, 等. 基于改进花授粉
University, 2013(in Chinese) [杜伟. 重载铁路曲线段轮轨磨耗影 算法和极限学习机的锅炉NO x 排放优化[J]. 动力工程学报, 2018,
响因素分析[D]. 成都: 西南交通大学, 2013]. 38(10): 782–787].
[ 7 ] Wang Xueping, Zhang Jun, Ma He. Research on prediction method [17] Zhang Wandong, Li Qingzhong, Li Ming, et al. Sea surface target
of wheel tread wear of high-speed train[J]. Tribology, 2018, 38(4): detection algorithm of high frequency ground wave radar RD
462–467 (in Chinese) [王雪萍, 张军, 马贺. 高速列车车轮踏面磨 spectrum based on optimal error self-tuning limit learning
耗预测方法的研究[J]. 摩擦学学报, 2018, 38(4): 462–467]. doi: machine[J]. Acta Automatica Sinica, 2019: 1–13 (in Chinese) [张万
10.16078/j.tribology.2018.04.011. 栋, 李庆忠, 黎明, 等. 基于最优误差自校正极限学习机的高频地
[ 8 ] Zou Xiaochun, Zhang Jun, Sun Chuanxi, et al. Simulation 波雷达RD谱图海面目标检测算法[J]. 自动化学报, 2019: 1–13].
calculation and experimental research on contact between doi: 10.16383/j.aas.c180210.
locomotive wheel tread and rail[J]. Tribology, 2020, 40(1): 128–134 [18] Singh Y, Chandra P. A class+1 sigmoidal activation functions for
(in Chinese) [邹小春, 张军, 孙传喜, 等. 机车车轮踏面与钢轨接触 FFANNs[J]. Journal of Economic Dynamics and Control, 2003,
的仿真计算及试验研究[J]. 摩擦学学报, 2020, 40(1): 128–134]. 28(1): 183–187. doi: 10.1016/s0165-1889(02)00157-4.
doi: 10.16078/j.tribology.2019146. [19] Soderstrom T, Stewart G W. On the numerical properties of an
[ 9 ] Wang Wenjian. Research on the coupling relationship between iterative method for computing the moore-penrose generalized
wheel rail rolling contact fatigue and wear and preventive inverse[J]. SIAM Journal on Numerical Analysis, 1974, 11(1):
measures[D]. Chengdu: Southwest Jiaotong University, 2008(in 61–74. doi: 10.2307/2156431.
Chinese) [王文健. 轮轨滚动接触疲劳与磨损耦合关系及预防措施 [20] Shinozaki N, Sibuya M, Tanabe K, et al. Numerical algorithms for
研究[D]. 成都: 西南交通大学, 2008]. the Moore-Penrose inverse of a matrix: Direct methods[J]. Annals of
[10] Kumar A, Singh D. Artificial neural network-based wear loss the Institute of Statistical Mathematics, 1972, 24(1): 193–203. doi:
prediction for A390 aluminum alloy[J]. Theor Appl Inf Technol, 10.1007/BF02479751.
2008: 961–964. [21] Huang G B, Zhou H, Ding X, et al. Extreme Learning Machine for
[11] Khudhair A, Talib N A. Neural network analysis for sliding wear of Regression and Multiclass Classification[C]. Systems Man and
13% Cr steel coatings by electric arc spraying[C]. First Engineering Cybernetics, 2012, 42(2): 513-529. doi: 10.1109/tsmcb.2011.2168
Scientific Conference College of Engineering–University of Diyal, 604
2010. [22] Xu W X, Geng Z Q, Zhu Q X, et al. A piecewise linear chaotic map
[12] Wang Ping, Wang Caiyun, Wang Wenjian, et al. Application of BP and sequential quadratic programming based robust hybrid particle
network in prediction of rail wear based on PSO Hybrid swarm optimization[J]. Information Sciences, 2013, 218(1): 85–102.
Algorithm[J]. Mechanical Design, 2013, 30(8): 15–20 (in Chinese) doi: 10.1016/j.ins.2012.06.003.
[王平, 王彩芸, 王文健, 等. 基于PSO混合算法优化BP网络在钢轨 [23] Dehdari V, Oliver D S, Deutsch C V, et al. Comparison of
磨损量预测中的应用[J]. 机械设计, 2013, 30(8): 15–20]. doi: optimization algorithms for reservoir management with constraints-
10.3969/j.issn.1001-2354.2013.08.004. A case study[J]. Journal of Petroleum Science and Engineering,
[13] Shebani A, Iwnicki S. Prediction of wheel and rail wear under 2012, 100: 41–49. doi: 10.1016/j.petrol.2012.11.013.
different contact conditions using artificial neural networks[J]. [24] Wu Bin, Liu Bin, Zeng Zhiping, et al. Experimental study on