[1] 1 Hu H X, Tang B, Gong X J. Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Transactions on Industrial Informatics, 2017, 13(4): 2106−2116 doi: 10.1109/TII.2017.2683528
[2] 2 Pan P Y, Hu H T, Yang X W, Blaabjerg F, Wang X F, He Z Y. Impedance measurement of traction network and electric train for stability analysis in high-speed railways. IEEE Transactions on Power Electronics, 2018, 33(12): 10086−10100 doi: 10.1109/TPEL.2018.2836660
[3] 周东华, 纪洪泉, 何潇. 高速列车信息控制系统的故障诊断技术. 自动化学报, 2018, 44(7): 1153−1164

3 Zhou Dong-Hua, Ji Hong-Quan, He Xiao. Fault diagnosis techniques for the information control system of high-speed trains. Acta Automatica Sinica, 2018, 44(7): 1153−1164
[4] 4 Zhang K P, Jiang B, Tao G. Mimo evolution model-based coupled fault estimation and adaptive control with high-speed train applications. IEEE Transactions on Control Systems Technology, 2018, 26(5): 1552−1566 doi: 10.1109/TCST.2017.2735360
[5] 5 Ning B, Dong H R, Gao S G. Distributed cooperative control of multiple high-speed trains under a moving block system by nonlinear mapping-based feedback. Science China (Information Sciences), 2018, 61(12): 22−33
[6] 6 Dong H R, Gao S G, Ning B. Cooperative control synthesis and stability analysis of multiple trains under moving signaling systems. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2730−2738 doi: 10.1109/TITS.2016.2518649
[7] 7 Song Q, Song Y D, Tang T. Computationally inexpensive tracking control of high-speed trains with traction/braking saturation. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1116−1125 doi: 10.1109/TITS.2011.2143409
[8] 8 Song Y D, Song Q, Cai W C. Fault-tolerant adaptive control of high-speed trains under traction/braking failures: a virtual parameter-based approach. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 737−748 doi: 10.1109/TITS.2013.2290310
[9] 9 Su S, Tang T, Roberts C. A cooperative train control model for energy saving. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 622−631 doi: 10.1109/TITS.2014.2334061
[10] 10 Song Q, Song Y D. Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures. IEEE Transactions on Neural Networks, 2011, 22(12): 2250−2261 doi: 10.1109/TNN.2011.2175451
[11] 郭红戈, 谢克明. 动车组列车制动系统的 Hammerstein 模型及其参数辨识方法. 铁道学报, 2014, 36(4): 48−53 doi: 10.3969/j.issn.1001-8360.2014.04.009

11 Guo Hong-Ge, Xie Ke-Ming. Hammerstein model and parameters identification of EMU braking system. Journal of the China Railway Society, 2014, 36(4): 48−53 doi: 10.3969/j.issn.1001-8360.2014.04.009
[12] 12 Ding F, Xu L, Alsaadi F E. Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique. Iet Control Theory and Applications, 2018, 12(7): 892−899 doi: 10.1049/iet-cta.2017.0821
[13] 13 Ding F, Liu X G, Chu J. Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle. Iet Control Theory and Applications, 2013, 7(2): 176−184 doi: 10.1049/iet-cta.2012.0313
[14] 14 Chon T B, Wills A, Ninness B. System identification of nonlinear state-space models. Automatica, 2011, 47(1): 39−49 doi: 10.1016/j.automatica.2010.10.013
[15] 15 Ding F, Liu G, Liu X P. Parameter estimation with scarce measurements. Automatica, 2011, 47(8): 1646−1655 doi: 10.1016/j.automatica.2011.05.007
[16] 16 Ding F, Wang F F, Xu L. Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique. IET Control Theory and Applications, 2017, 11(3): 390−400 doi: 10.1049/iet-cta.2016.0491
[17] 17 Ding F, Chen T W. Performance analysis of multi-innovation gradient type identification methods. Automatica, 2007, 43(1): 1−14 doi: 10.1016/j.automatica.2006.07.024
[18] 18 Ma P, Ding F, Zhu Q M. Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation. International Journal of Systems Science, 2018, 49(5): 920−928 doi: 10.1080/00207721.2018.1433247
[19] 19 Ding F, Chen T. Hierarchical gradient-based identification of multivariable discrete-time systems. Automatica, 2005, 41(2): 315−325 doi: 10.1016/j.automatica.2004.10.010
[20] 20 Zhang X, Ding F, Xu L. State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle. IET Control Theory and Applications, 2018, 12(12): 1704−1713 doi: 10.1049/iet-cta.2018.0156
[21] 21 Ding F, Liu G J, Liu X P. Partially coupled stochastic gradient identification methods for non-uniformly sampled systems. IEEE Transactions on Automatic Control, 2010, 55(8): 1976−1981 doi: 10.1109/TAC.2010.2050713
[22] 22 Wang X, Ding F. Partially coupled extended stochastic gradient algorithm for nonlinear multivariable output error moving average systems. Engineering Computations, 2017, 34(2): 629−647 doi: 10.1108/EC-05-2015-0126
[23] 衷路生, 李兵, 龚锦红. 高速列车非线性模型的极大似然辨识. 自动化学报, 2014, 40(12): 2950−2958

23 Zhong Lu-Sheng, Li Bing, Gong Jin-Hong. Maximum likelihood identification of nonlinear model for high-speed train. Acta Automatica Sinica, 2014, 40(12): 2950−2958
[24] 谢国, 张丹, 黑新宏. 高速列车纵向动力学模型时变参数在线辨识方法. 交通运输工程学报, 2017, 17(1): 71−81 doi: 10.3969/j.issn.1671-1637.2017.01.009

24 Xie Guo, Zhang Dan, Hei Xin-Hong. Online identification method of time-varying parameters for longitudinal dynamics model of high-speed train. Journal of Traffic and Transportation Engineering, 2017, 17(1): 71−81 doi: 10.3969/j.issn.1671-1637.2017.01.009
[25] 25 Noh S J, Shim D, Jeon M. Adaptive sliding-window strategy for vehicle detection in highway environments. Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments, 2016, 17(2): 323−335
[26] 26 Shakil S, Billings J C, Keiholz S D. Parametric dependencies of sliding window correlation. IEEE Transactions on Biomedical Engineering, 2018, 65(2): 254−263 doi: 10.1109/TBME.2017.2762763
[27] 27 Zhang Y Q, Lou Y C, Hong Y G. An approximate gradient algorithm for constrained distributed convex optimization. IEEE/CAA Journal of Automatica Sinica, 2015, 1(1): 61−67