Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 246964, 11 pages
http://dx.doi.org/10.1155/2014/246964
Research Article

Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

1Department of Electromechanical Engineering, University of Macau, Macau
2Department of Computer and Information Science, University of Macau, Macau

Received 21 January 2014; Accepted 6 March 2014; Published 7 April 2014

Academic Editor: Qingsong Xu

Copyright © 2014 Pak Kin Wong 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. F. C. Chen, “Back-propagation neural networks for nonlinear self-tuning adaptive control,” IEEE Control Systems Magazine, vol. 10, no. 3, pp. 44–48, 1990. View at Google Scholar · View at Scopus
  2. C. Y. Lee and J. J. Lee, “Adaptive Control for Uncertain Nonlinear Systems Based on Multiple Neural Networks,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 34, no. 1, pp. 325–333, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. J. Liu, C. L. P. Chen, G. X. Wen, and S. Tong, “Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems,” IEEE Transactions on Neural Networks, vol. 22, no. 7, pp. 1162–1167, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. C. C. Tsai, H. C. Huang, and S. C. Lin, “Adaptive neural network control of a self-balancing two-wheeled scooter,” IEEE Transactions on Industrial Electronics, vol. 57, no. 4, pp. 1420–1428, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Xiaofang, W. Yaonan, S. Wei, and W. Lianghong, “RBF networks-based adaptive inverse model control system for electronic throttle,” IEEE Transactions on Control Systems Technology, vol. 18, no. 3, pp. 750–756, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Z. Peng and R. Dubay, “Identification and adaptive neural network control of a DC motor system with dead-zone characteristics,” ISA Transactions, vol. 50, no. 4, pp. 588–598, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. L. S. Guo and L. Parsa, “Model reference adaptive control of five-phase IPM motors based on neural network,” IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1500–1508, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Q. Xia, X. C. Shao, A. Zhao, and H. Y. Wu, “Adaptive neural network control with backstepping for surface ships with input dead-zone,” Mathematical Problems in Engineering, vol. 2013, Article ID 530162, 9 pages, 2013. View at Publisher · View at Google Scholar
  9. Y. LeCun, L. Bottou, G. B. Orr, and K. R. Müller, “Efficient backprop,” in Neural Networks: Tricks of the Trade, G. B. Orr and K. R. Müller, Eds., pp. 9–50, Springer, Berlin, Germany, 1998. View at Google Scholar
  10. H. J. Rong and G. S. Zhao, “Direct adaptive neural control of nonlinear systems with extreme learning machine,” Neural Computing and Applications, vol. 22, pp. 577–586, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. K. I. Wong, P. K. Wong, C. S. Cheung, and C. M. Vong, “Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set,” in Applied Soft Computing, vol. 13, pp. 4428–4441, 2013. View at Google Scholar
  12. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 985–990, Budapest, Hungary, July 2004. View at Scopus
  13. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. C. J. Lu and Y. J. E. Shao, “Forecasting computer products sales by integrating ensemble empirical mode decomposition and extreme learning machine,” Mathematical Problems in Engineering, vol. 2012, Article ID 831201, 15 pages, 2012. View at Publisher · View at Google Scholar
  15. K. I. Wong, P. K. Wong, C. S. Cheung, and C. M. Vong, “Modeling and optimization of biodiesel engine performance using advanced machine learning methods,” Energy, vol. 55, pp. 519–528, 2013. View at Google Scholar
  16. P. K. Wong, C. M. Vong, C. S. Cheung, and K. I. Wong, “Diesel engine modelling using extreme learning machine under scarce and exponential data sets,” International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, vol. 21, supplement 2, pp. 87–98, 2013. View at Google Scholar
  17. Y. Lan, Z. J. Hu, Y. C. Soh, and G. B. Huang, “An extreme learning machine approach for speaker recognition,” Neural Computing and Applications, vol. 22, pp. 417–425, 2013. View at Google Scholar
  18. A. Iosifidis, A. Tefas, and I. Pitas, “Minimum class variance extreme learning machine for human action recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, pp. 1968–1979, 2013. View at Google Scholar
  19. N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411–1423, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. H. T. Huynh and Y. Won, “Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks,” Pattern Recognition Letters, vol. 32, no. 14, pp. 1930–1935, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. W. Deng, Q. Zheng, and C. Lin, “Regularized extreme learning machine,” in Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM '09), pp. 389–395, Nashville, Tenn, USA, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. G. B. Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, vol. 74, no. 1–3, pp. 155–163, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. G. B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 42, no. 2, pp. 513–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” IEEE Transactions on Information Theory, vol. 44, no. 2, pp. 525–536, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  25. P. K. Wong, H. C. Wong, and C. M. Vong, “Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction,” International Journal of Engine Research, vol. 13, no. 1, pp. 28–40, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. G. Y. Li, Application of Intelligent Control and MATLAB to Electronically Controlled Engines, Publishing House of Electronics Industry, Beijing, China, 1 edition, 2007 (Chinese).