Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2011 (2011), Article ID 102436, 21 pages
http://dx.doi.org/10.1155/2011/102436
Research Article

Interval Type-2 Recurrent Fuzzy Neural System for Nonlinear Systems Control Using Stable Simultaneous Perturbation Stochastic Approximation Algorithm

Department of Electrical Engineering, Yuan Ze University, Chung-Li 32003, Taiwan

Received 31 August 2010; Revised 6 April 2011; Accepted 25 April 2011

Academic Editor: Geraldo Silva

Copyright © 2011 Ching-Hung Lee and Feng-Yu Chang. 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. Y. C. Chen and C. C. Teng, “A model reference control structure using a fuzzy neural network,” Fuzzy Sets and Systems, vol. 73, no. 3, pp. 291–312, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  2. C. H. Lee and C. C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 4, pp. 349–366, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Gao and M. J. Er, “Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 4, pp. 462–477, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. R. J. Wai and P. C. Chen, “Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 552–559, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Lotfi and A. C. Tsoi, “Learning fuzzy inference systems using an adaptive membership function scheme,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 26, no. 2, pp. 326–331, 1996. View at Publisher · View at Google Scholar · View at Scopus
  6. C. H. Lee and C. C. Teng, “Fine tuning of membership functions for fuzzy neural systems,” Asian Journal of Control, vol. 3, no. 3, pp. 216–225, 2001. View at Google Scholar · View at Scopus
  7. P. Z. Lin and T. T. Lee, “Robust self-organizing fuzzy-neural control using asymmetric Gaussian membership functions,” International Journal of Fuzzy Systems, vol. 9, no. 2, pp. 77–86, 2007. View at Google Scholar · View at Scopus
  8. C. H. Lee and H. Y. Pan, “Performance enhancement for neural fuzzy systems using asymmetric membership functions,” Fuzzy Sets and Systems, vol. 160, no. 7, pp. 949–971, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  9. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning-1,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  10. Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: theory and design,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 5, pp. 535–550, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. J. M. Mendel, “On the importance of interval sets in type-2 fuzzy logic systems,” in Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 1647–1652, Vancouver, BC, Canada, July 2001.
  12. C. H. Lee, J. L. Hong, Y. C. Lin, and W. Y. Lai, “Type-2 fuzzy neural network systems and learning,” International Journal of Computational Cognition, vol. 1, no. 4, pp. 79–90, 2003. View at Google Scholar
  13. C. H. Lee, Y. C. Lin, and W. Y. Lai, “Systems identification using type-2 fuzzy neural network (type-2 FNN) systems,” in Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA '03), vol. 3, pp. 1264–1269, 2003. View at Publisher · View at Google Scholar
  14. C. H. Lee and Y. C. Lin, “Control of nonlinear uncertain systems using type-2 fuzzy neural network and adaptive filter,” in Proceedings of the IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 1177–1182, March 2004. View at Scopus
  15. C. H. Lee and Y. C. Lin, “An adaptive type-2 fuzzy neural controller for nonlinear uncertain systems,” Control and Intelligent Systems, vol. 12, no. 1, pp. 41–50, 2005. View at Google Scholar
  16. C. H. Lee, F. Y. Chang, and C. T. Lee, “Species-based hybrid of electromagnetism-like mechanism and back-propagation algorithms for an interval type-2 fuzzy system design,” in Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS '10), Lecture Notes in Engineering and Computer Science, pp. 140–145, Hong Kong, March 2010.
  17. G. C. Mouzouris and J. M. Mendel, “Nonsingleton fuzzy logic systems: theory and application,” IEEE Transactions on Fuzzy Systems, vol. 5, no. 1, pp. 56–71, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Zhang and A. J. Morris, “Recurrent neuro-fuzzy networks for nonlinear process modeling,” IEEE Transactions on Neural Networks, vol. 10, no. 2, pp. 313–326, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. C. F. Juang, “A TSK-type recurrent fuzzy neural network for dynamic systems processing by neural network and genetic algorithm,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 155–170, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. J. B. Theocharis, “A high-order recurrent neuro-fuzzy system with internal dynamics: application to the adaptive noise cancellation,” Fuzzy Sets and Systems, vol. 157, no. 4, pp. 471–500, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. J. C. Spall, “Multivariate stochastic approximation using a simultaneous perturbation gradient approximation,” IEEE Transactions on Automatic Control, vol. 37, no. 3, pp. 332–341, 1992. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. J. C. Spall and J. A. Cristion, “Model free control of nonlinear stochastic systems with discrete-time measurements,” IEEE Transactions on Automatic Control, vol. 43, no. 9, pp. 1198–1210, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  23. J. C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, John Wiley & Sons, Hoboken, NJ, USA, 2003.
  24. N. N. Karnik and J. M. Mendel, “Centroid of a type-2 fuzzy set,” Information Sciences, vol. 132, no. 1–4, pp. 195–220, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  25. J. M. Mendel, Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper Saddle River, NJ, USA, 2001. View at Zentralblatt MATH
  26. J. R. Castro, O. Castillo, P. Melin, and R. Díaz, “A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks,” Information Sciences, vol. 179, no. 13, pp. 2175–2193, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  27. J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice-Hall, Upper Saddle River, NJ, USA, 1991.
  28. T. Yabuta and T. Yamada, “Learning control using neural networks,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '91), pp. 740–745, Sacramento, Calif, USA, April 1991. View at Scopus
  29. Y. H. Joo, L. S. Shieh, and G. Chen, “Hybrid state space fuzzy model based controller with dual-rate sampling for digital control of chaotic systems,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 4, pp. 394–408, 1999. View at Publisher · View at Google Scholar · View at Scopus
  30. C. H. Wang, T. C. Lin, T. T. Lee, and H. L. Liu, “Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 32, no. 5, pp. 583–597, 2002. View at Publisher · View at Google Scholar · View at Scopus