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Mathematical Problems in Engineering
Volume 2015, Article ID 125868, 7 pages
http://dx.doi.org/10.1155/2015/125868
Research Article

Twin Support Vector Machine Method for Identification of Wiener Models

King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Received 7 January 2015; Revised 11 April 2015; Accepted 16 April 2015

Academic Editor: Shiliang Sun

Copyright © 2015 Mujahed Al-Dhaifallah. 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. J. Schoukens, R. Pintelon, T. Dobrowiecki, and Y. Rolain, “Identification of linear systems with nonlinear distortions,” Automatica, vol. 41, no. 3, pp. 491–504, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. D. Westwick and R. Kearney, Identification of Nonlinear Physiological Systems, John Wiley & Sons, Piscataway, NJ, USA, 2003.
  3. D. Westwick and M. Verhaegen, “Identifying MIMO Wiener systems using subspace model identification methods,” Signal Processing, vol. 52, no. 2, pp. 235–258, 1996. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  4. D. Q. Wang and F. Ding, “Hierarchical least squares estimation algorithm for hammerstein-wiener systems,” IEEE Signal Processing Letters, vol. 19, no. 12, pp. 825–828, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Zhou, X. Li, and F. Pan, “Least-squares-based iterative identification algorithm for Wiener nonlinear systems,” Journal of Applied Mathematics, vol. 2013, Article ID 565841, 6 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  6. F. Ding, J. Ma, and Y. Xiao, “Newton iterative identification for a class of output nonlinear systems with moving average noises,” Nonlinear Dynamics, vol. 74, no. 1-2, pp. 21–30, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. Y. Hu, B. Liu, Q. Zhou, and C. Yang, “Recursive extended least squares parameter estimation for Wiener nonlinear systems with moving average noises,” Circuits, Systems, and Signal Processing, vol. 33, no. 2, pp. 655–664, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. M. Schoukens, G. Vandersteen, Y. Rolain, and F. Ferranti, “Fast identification of Wiener-Hammerstein systems using discrete optimisation,” Electronics Letters, vol. 50, no. 25, pp. 1942–1944, 2014. View at Publisher · View at Google Scholar
  9. M. Schoukens, A. Marconato, R. Pintelon, G. Vandersteen, and Y. Rolain, “Parametric identification of parallel Wiener-Hammerstein systems,” Automatica, vol. 51, pp. 111–122, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  10. S. L. Lacy and D. S. Bernstein, “Identification of FIR Wiener systems with unknown, noninvertible, polynomial nonlinearities,” in Proceedings of the American Control Conference, pp. 893–898, May 2002. View at Scopus
  11. M. Pawlak, Z. Hasiewicz, and P. Wachel, “On nonparametric identification of Wiener systems,” IEEE Transactions on Signal Processing, vol. 55, no. 2, pp. 482–492, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. V. N. Vapnik, Statisical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998. View at MathSciNet
  13. J. A. K. Suykens, T. van Gestel, J. de Brabanter, B. de Moor, and J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.
  14. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, UK, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  15. P. Xinjun, “TSVR: an efficient twin support vector machine for regression,” Neural Networks, vol. 23, no. 3, pp. 365–372, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Tian and Z. Qi, “Review on: twin support vector machines,” Annals of Data Science, vol. 1, no. 2, pp. 253–277, 2014. View at Publisher · View at Google Scholar
  17. Z. Qi, Y. Tian, and Y. Shi, “Laplacian twin support vector machine for semi-supervised classification,” Neural Networks, vol. 35, pp. 46–53, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. X. Yang, “Nonparallel hyperplanes proximal classifiers based on manifold regularization for labeled and unlabeled examples,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 5, Article ID 1350015, pp. 1–19, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Tötterman and H. T. Toivonen, “Support vector method for identification of Wiener models,” Journal of Process Control, vol. 19, no. 7, pp. 1174–1181, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Ljung, System Identification: Theory for the User, Prentice Hall PTR, Upper Saddle River, NJ, USA, 1999.
  21. S. A. AlSabbah, M. A. Al-Khedher, M. K. Abu Zalata, and T. M. Younes, “Evaluation of multiregional fuzzy cascade control for pH neutralization process,” International Journal of Research Reviews in Applied Sciences, vol. 10, no. 2, pp. 193–199, 2012. View at Google Scholar · View at MathSciNet