Changfan Zhang, Xiang Cheng, Jing He, and Guangwei Liu


  1. [1] W. Liao, H. Chen, W. Cai, et al., A novel active adhesioncontrol design for high speed trains without vehicle speedmeasurement, 33rd China Control Conference (CCC), Nanjing,China, 2014, 221–226.
  2. [2] K. Berntorp, Joint wheel-slip and vehicle-motion estimationbased on inertial, GPS, and wheel-speed sensors, IEEE Trans-actions on Control Systems Technology, 24(3), 2016, 1020–1027.
  3. [3] P. Pichl´ık, O. Zoubek, and J. Zdˇenek, Measuring device formeasurement of train dynamic motion during wheel slip, 2014IEEE Int. Conf. on Applied Electronics (AE), University ofWest Bohemia, Pilsen, Czech Republic, 2014, 247–250.
  4. [4] S. Sadr, D.A. Khaburi, and J. Rodr´ıguez, Predictive slipcontrol for electrical trains, IEEE Transactions on IndustrialElectronics, 63(6), 2016, 3446–3457.
  5. [5] C.Y. Jae, Analysis of spectrum occupancy using machine learn-ing algorithms, IEEE Transactions on Vehicular Technology,83(331), 2015, 577–583.
  6. [6] N. Li, X. Feng, and X. Wei, Optimized adhesion control oflocomotive airbrake based on GSA-RNN, 2015 7th IEEE Int.Conf. on Intelligent Human–Machine Systems and Cybernetics(IHMSC), Hangzhou, China, 2015, 157–161.
  7. [7] G. Zhuo and B. Wang, Structure designing of BP neural networkin the application of reference velocity estimation, 2014 IEEEInt. Conf. on Mechatronics and Automation, Tianjin, China,2014, 1481–1485.
  8. [8] H. Chen, W. Cai, and Y. Song, Wheel skid prediction andantiskid control of high speed trains, 2014 IEEE Int. Conf. onIntelligent Transportation Systems (ITSC), Qingdao, China,2014, 1209–1214.
  9. [9] A. Suebsomran, Adaptive neural network control of electro-magnetic suspension system, International Journal of Roboticsand Automation, 29(2), 2014, 144–154.
  10. [10] L. Tong, F. Zhang, Z.G. Hou, et al., BP-AR-based human jointangle estimation using multi-channel SEMG, InternationalJournal of Robotics and Automation, 30(3), 2015, 227–237.
  11. [11] G.B. Huang, H. Zhou, X. Ding, et al., Extreme learning machinefor regression and multiclass classification, IEEE Transactionson Systems, Man, and Cybernetics, Part B (Cybernetics),42(2), 2012, 513–529.
  12. [12] T.T. Teo, T. Logenthiran, and W.L. Woo, Forecasting ofphotovoltaic power using extreme learning machine, 2015IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA),Bangkok, Thailand, 2015, 1–6.
  13. [13] Z. Bai, G.B. Huang, D. Wang, et al., Sparse extreme learningmachine for classification, IEEE Transactions on Cybernetics,44(10), 2014, 1858–1870.
  14. [14] A. Mozaffari and N.L. Azad, Optimally pruned extreme learn-ing machine with ensemble of regularization techniques andnegative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification, Neurocomputing,131, 2014, 143–156.199
  15. [15] S. Salcedo-Sanz, C. Casanova-Mateo, A. Pastor-S´anchez, et al.,Daily global solar radiation prediction based on a hybrid coralreefs optimization – extreme learning machine approach, SolarEnergy, 105, 2014, 91–98.
  16. [16] T. Koseki and T. Hara, Compensation of excessive angular mo-mentum in a re-adhesion control of an electric train, 2015 Int.Conf. Electrical Systems for Aircraft, Railway, Ship Propul-sion and Road Vehicles (ESARS), RWTH Aachen University,Aachen, Germany, 2015, 1–6.
  17. [17] Y. Yao, S. Zhao, F. Xiao, and J. Liu, The effects of wheelsetdriving system suspension parameters on the re-adhesion per-formance of locomotives, Vehicle System Dynamics, 53(12),2015, 1935–1951.
  18. [18] G.B. Huang, Q.Y. Zhu, and C.K. Siew, Extreme learningmachine: Theory and applications, Neurocomputing, 70(1),2006, 489–501.
  19. [19] H.-J. Rong, Y.-S. Ong, A.-H. Tan, and Z. Zhu, A fast pruned-extreme learning machine for classification problem, Neuro-computing, 72(1), 2008, 359–366.
  20. [20] G.B. Huang, Q.Y. Zhu, and C.K. Siew, Extreme learning ma-chine: A new learning scheme of feed forward neural networks,Proc. 2004 IEEE Int. Joint Conf., Budapest, Hungary, 2004,985–990.
  21. [21] N. Jianjun, X. Yang, J. Chen, and S.X. Yang, Dynamic bioin-spired neural network for multi-robot formation control inunknown environments, International Journal of Roboticsand Automation, 30(3), 2015. DOI:10.2316/Journal.206.2015.3.206-4217
  22. [22] J. Zhang, F. Tian, S.X. Yang, Y. Liu, Z. Liang, and D. Wang,An intelligent and automatic control method for tobaccoflue-curing based on machine learning, International Jour-nal of Robotics and Automation, 31(6), 2016. DOI:10.2316/Journal.206.2016.6.206-4697
  23. [23] T. Huang, P. Yang, K. Yang, and Y. Zhu, Navigation of mobilerobot in unknown environment based on T–S neuro-fuzzysystem, International Journal of Robotics and Automation,30(4), 2015. DOI:10.2316/Journal.206.2015.4.206-4344
  24. [24] F. Liu and L. Fei, Time-jerk optimal planning of industrial robottrajectories, International Journal of Robotics and Automation,31(1), 2016. DOI:10.2316/Journal.206.2016.1.206-4055
  25. [25] P. He and S. Dai, Real-time stealth corridor path planning forfleets of unmanned aerial vehicles in low-altitude penetration,International Journal of Robotics and Automation, 30(1), 2015,60–69.
  26. [26] F. Martın, L. Moreno, M.L. Munoz, et al., Initial population sizeestimation for a differential-evolution-based global localizationfilter, International Journal of Robotics and Automation, 29(3),2014, 245–258.
  27. [27] A. Maram, I. Chaari, A. Koubaa, et al., Global robot pathplanning using GA for large grid maps: Modelling, perfor-mance and experimentation, International Journal of Roboticsand Automation, 31(6), 2016. DOI:10.2316/Journal.206.2016.6.206-4602

Important Links:

Go Back