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Mathematical Problems in Engineering
Volume 2014, Article ID 172549, 7 pages
http://dx.doi.org/10.1155/2014/172549
Research Article

Station Stopping of Freight Trains with Pneumatic Braking

1MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China
2Department of Computer Science, Shantou University, 243 Daxue Road, Shantou 515063, China

Received 23 April 2014; Accepted 22 June 2014; Published 7 July 2014

Academic Editor: Haipeng Peng

Copyright © 2014 Yun Bai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Liu and I. M. Golovitcher, “Energy-efficient operation of rail vehicles,” Transportation Research A, vol. 37, no. 10, pp. 917–932, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Tuyttens, H. Fei, M. Mezmaz, and J. Jalwan, “Simulation-based genetic algorithm towards an energy-efficient railway traffic control,” Mathematical Problems in Engineering, vol. 2013, Article ID 805410, 12 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Bai, T. K. Ho, B. H. Mao, Y. Ding, and S. K. Chen, “Energy-efficient locomotive operation for Chinese mainline railways by fuzzy predictive control,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 3, pp. 938–948, 2014. View at Google Scholar
  4. Y. Bai, T. Ho, and B. Mao, “Train control to reduce delays upon service disturbances at railway junctions,” Journal of Transportation Systems Engineering and Information Technology, vol. 11, no. 5, pp. 114–122, 2011. View at Google Scholar · View at Scopus
  5. H. Oshima, S. Yasunobu, and S. I. Sekino, “Automatic train operation system based on predictive fuzzy control,” in Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications, pp. 485–489, Hitachi City, Japan, May 1988.
  6. H. Yang, Y. T. Fu, K. P. Zhang, and Z. Q. Li, “Speed tracking control using an ANFIS model for high-speed electric multiple unit,” Control Engineering Practice, vol. 23, pp. 57–65, 2014. View at Google Scholar
  7. W. Wang, W. Zhang, H. Guo, H. Bubb, and K. Ikeuchi, “A safety-based approaching behavioural model with various driving characteristics,” Transportation Research C, vol. 19, no. 6, pp. 1202–1214, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. M. McClanachan and C. Cole, “Current train control optimization methods with a view for application in heavy haul railways,” Proceedings of the Institution of Mechanical Engineers F, vol. 226, no. 1, pp. 36–47, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. V. Bocharnikov, A. M. Tobias, C. Roberts, S. Hillmansen, and C. J. Goodman, “Optimal driving strategy for traction energy saving on DC suburban railways,” IET Electric Power Applications, vol. 1, no. 5, pp. 675–682, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Lukaszewicz, Energy consumption and running time for trains [Ph.D. thesis], Royal Institute of Technology, Stockholm, Sweden, 2001.
  11. T. K. Ho, B. H. Mao, Z. Z. Yuan, H. D. Liu, and Y. F. Fung, “Computer simulation and modeling in railway applications,” Computer Physics Communications, vol. 143, no. 1, pp. 1–10, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Yasunobu, S. Miyamoto, and H. Ihara, “A fuzzy control for train automatic stop control,” Transactions of the Society of Instrument and Control Engineers, vol. 2, no. 1, pp. 1–9, 2002. View at Google Scholar
  13. Z. Hou, Y. Wang, C. Yin, and T. Tang, “Terminal iterative learning control based station stop control of a train,” International Journal of Control, vol. 84, no. 7, pp. 1263–1274, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  14. D. Chen and C. Gao, “Soft computing methods applied to train station parking in urban rail transit,” Applied Soft Computing Journal, vol. 12, no. 2, pp. 759–767, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Chen, R. Chen, Y. Li, and T. Tang, “Online learning algorithms for train automatic stop control using precise location data of balises,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1526–1535, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, New Jersey, NJ, USA, 1992. View at MathSciNet
  17. X. Guan, Z. Fan, H. Peng, and Y. Wang, “The synchronization of chaotic systems based on RBF network in the presence of perturbation,” Acta Physica Sinica, vol. 50, no. 9, pp. 1673–1674, 2001. View at Google Scholar · View at Scopus
  18. W. P. Wang, L. X. Li, H. P. Peng, J. H. Xiao, and Y. X. Yang, “Synchronization control of memristor-based recurrent neural networks with perturbations,” Neural Networks, vol. 53, pp. 8–14, 2014. View at Google Scholar
  19. L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, no. 3, pp. 338–353, 1965. View at Google Scholar · View at MathSciNet · View at Scopus
  20. J. M. Keller and H. Tahani, “Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks,” International Journal of Approximate Reasoning, vol. 6, no. 2, pp. 221–240, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  21. J. J. Buckley and Y. Hayashi, “Fuzzy neural networks: a survey,” Fuzzy Sets and Systems, vol. 66, no. 1, pp. 1–13, 1994. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. J. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Yager and D. Filev, “Generation of fuzzy rules by mountain clustering,” Journal of Intelligent and Fuzzy Systems, vol. 2, no. 3, pp. 209–219, 1994. View at Google Scholar
  24. R. Rojas, Neural Networks—A Systematic Introduction, Springer, Berlin, Germany, 1996.
  25. B. Zhong, “Endurance of mid-phosphorous cast iron,” Railway Vehicle, no. 7, pp. 30–32, 1991. View at Google Scholar
  26. H. Peng, N. Wei, L. Li, W. Xie, and Y. Yang, “Models and synchronization of time-delayed complex dynamical networks with multi-links based on adaptive control,” Physics Letters A, vol. 374, no. 23, pp. 2335–2339, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. C. Xia, Z. Wang, J. Sanz, S. Meloni, and Y. Moreno, “Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks,” Physica A: Statistical Mechanics and Its Applications, vol. 392, no. 7, pp. 1577–1585, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus