Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2018, Article ID 1067927, 12 pages
https://doi.org/10.1155/2018/1067927
Research Article

Vehicle Information Influence Degree Screening Method Based on GEP Optimized RBF Neural Network

1Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China
2Guangzhou Customs, Guangzhou 510623, China
3South China Agricultural University, Guangzhou 510642, China
4Guangzhou Xingwei Mdt InfoTech Ltd, Guangzhou 510630, China
5School of Computer Software in Tianjin University, Tianjin 300072, China
6North China Electric Power University, Beijing 102206, China
7College of Mechanical and Electrical Engineering, Foshan University, Foshan 528000, China

Correspondence should be addressed to Lufeng Luo; nc.ude.usof@gnefuloul

Received 23 March 2018; Revised 27 June 2018; Accepted 8 July 2018; Published 14 October 2018

Academic Editor: Andy Annamalai

Copyright © 2018 Jingfeng Yang 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. P. Chuanqin and B. Yufeng, “Study on influence factors screening of highway traffic safety evaluation,” Highway Traffic Science and Technology (Applied Technology Edition), vol. 22, no. 11, pp. 246–248, 2015. View at Google Scholar
  2. S. Wang, F. Ma, and C. Jun, “Influencing factor analysis on typical urban passenger transport mode choice,” Journal of Transportation Systems Engineering and Information Technology, vol. 10, no. 3, pp. 93–98, 2010. View at Google Scholar
  3. C. Xu, Z. Qu, P. Tao, and S. Jin, “Methods of real-time screening and reconstruction for dynamic traffic abnormal data,” Journal of Harbin Engineering University, vol. 37, no. 2, pp. 211–217, 2016. View at Google Scholar
  4. P. Yulong and J. Ma, “Real-time traffic data screening and reconstruction,” China Civil Engineering Journal, vol. 36, no. 7, pp. 78–83, 2003. View at Google Scholar
  5. L. Gang, G. Jiang, X. Zhang, and J. Wang, “Screening and checking for ITS traffic sensor data,” Journal of Jilin University: Engineering and Technology Edition, vol. 34, no. 1, pp. 122–126, 2004. View at Google Scholar
  6. P. Yulong and B. Junying, “Selection of the key influencing factors of urbantraffic structure evolution,” Journal of Transport Science and Engineering, vol. 33, no. 1, pp. 66–71, 2017. View at Google Scholar
  7. H. Li, X. Peng, Z. Zhong, Z. Bai, and H. Hong, “Influence factors and control countermeasures of traffic noise based on grey relational analysis,” Noise and Vivration Control, vol. 32, no. 1, pp. 93–95, 2012. View at Google Scholar
  8. Z. Yang and L. Xiaoming, “The eflect of urban travel cost on travel structure,” Journal of Transportation Systems Engineering and Infomation Technology, vol. 12, no. 2, pp. 1–26, 2012. View at Google Scholar
  9. C. Yang, Z. Li, R. Cui, and B. Xu, “Neural network-based motion control of an under-actuated wheeled inverted pendulum model,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 11, pp. 2004–2016, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Yang, Y. Jiang, Z. Li, W. He, and C. Y. Su, “Neural control of bimanual robots with guaranteed global stability and motion precision,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1162–1171, 2017. View at Publisher · View at Google Scholar · View at Scopus
  11. C. Yang, X. Wang, L. Cheng, and H. Ma, “Neural-learning based telerobot control with guaranteed performance,” IEEE Transactions on Cybernetics, vol. 47, no. 10, pp. 3148–3159, 2017. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Yang, X. Wang, Z. Li, Y. Li, and C. Y. Su, “Teleoperation control based on combination of wave variable and neural networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 2125–2136, 2017. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Xu, Z. Shi, C. Yang, and F. Sun, “Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2626–2634, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Xu and F. Sun, “Composite intelligent learning control of strict-feedback systems with disturbance,” IEEE Transactions on Cybernetics, vol. 48, no. 2, pp. 730–741, 2018. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Junjian, Y. Changan, and C. Hongguo, “Enhanced RBF neural network algorithm based on gene expression programming,” Journal of Chinese Computer Systems, vol. 31, no. 5, pp. 950–954, 2010. View at Google Scholar
  16. C. Ferreira, Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, Springer-Verlag, Berlin Heidelberg, 2nd edition edition, 2006.
  17. S. Deng, C. Yuan, B. Zhao et al., “Attribution reduction classification algorithms based on GEP and neural network,” Computer Engineering and Applications, vol. 23, pp. 154–157, 2006. View at Google Scholar
  18. W. Li, C. Yang, Y. Jiang, X. Liu, and C. Y. Su, “Motion planning for omnidirectional wheeled mobile robot by potential field method,” Journal of Advanced Transportation, vol. 2017, Article ID 4961383, 11 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Li, C. Yang, C. Y. Su, J. Deng, and W. Zhang, “Vision-based model predictive control for steering of a nonholonomic mobile robot,” IEEE Transactions on Control Systems Technology, vol. 24, no. 2, pp. 553–564, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Yang, C. Zeng, P. Liang, Z. Li, R. Li, and C.-Y. Su, “Interface design of a physical human–robot interaction system for human impedance adaptive skill transfer,” IEEE Transactions on Automation Science and Engineering, vol. 15, no. 1, pp. 329–340, 2018. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Yang, K. Huang, H. Cheng, Y. Li, and C. Y. Su, “Haptic identification by ELM-controlled uncertain manipulator,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 2398–2409, 2017. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Yang, H. Wu, Z. Li, W. He, N. Wang, and C.-Y. Su, “Mind control of a robotic arm with visual fusion technology,” IEEE Transactions on Industrial Informatics, vol. 13, p. 1, 2017. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Zhao, Z. Liu, Z. Li, N. Wang, and J. Yang, “Control design for a vibrating flexible marine riser system,” Journal of the Franklin Institute, vol. 354, no. 18, pp. 8117–8133, 2017. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Zhao, X. Wang, C. Zhang, Z. Liu, and J. Yang, “Neural network based boundary control of a vibrating string system with input deadzone,” Neurocomputing, vol. 275, pp. 1021–1027, 2018. View at Publisher · View at Google Scholar · View at Scopus
  25. Z. Zhao, Y. Liu, and F. Luo, “Output feedback boundary control of an axially moving system with input saturation constraint,” ISA Transactions, vol. 68, pp. 22–32, 2017. View at Publisher · View at Google Scholar · View at Scopus
  26. Z. Zhao, J. Shi, X. Lan, X. Wang, and J. Yang, “Adaptive neural network control of a flexible string system with non-symmetric dead-zone and output constraint,” Neurocomputing, vol. 283, pp. 1–8, 2017. View at Publisher · View at Google Scholar · View at Scopus
  27. C. Yang, J. Luo, Y. Pan, Z. Liu, and C. Y. Su, “Personalized variable gain control with tremor attenuation for robot teleoperation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 99, pp. 1–12, 2017. View at Publisher · View at Google Scholar · View at Scopus
  28. Z. Zhao, Y. Liu, F. Luo, and W. He, “Adaptive boundary control of an axially moving belt system with high acceleration/deceleration,” IET Control Theory & Applications, vol. 10, no. 11, pp. 1299–1306, 2016. View at Publisher · View at Google Scholar · View at Scopus
  29. L. Wang, J. Yang, N. Zhang et al., “A spatial-temporal estimation model of residual energy for pure electric buses based on traffic performance index,” Tehnicki vjesnik - Technical Gazette, vol. 24, no. 6, pp. 1803–1811, 2017. View at Publisher · View at Google Scholar · View at Scopus
  30. L. Wang, J. Yang, N. Zhang et al., “Time-space relationship analysis model on the bus driving characteristics of different drivers based on the traffic performance index system,” Tehnicki vjesnik - Technical Gazette, vol. 25, no. 1, pp. 236–244, 2018. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Wang, S. Lin, J. Yang et al., “Dynamic traffic congestion simulation and dissipation control based on traffic flow theory model and neural network data calibration algorithm,” Complexity, vol. 2017, Article ID 5067145, 11 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus