Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 735310, 19 pages
http://dx.doi.org/10.1155/2014/735310
Research Article

An Interval Type-2 Fuzzy System with a Species-Based Hybrid Algorithm for Nonlinear System Control Design

1Department of Electrical Engineering, Yuan Ze University, Taiwan
2Department of Mechanical Engineering, National Chung Hsing University, Taiwan

Received 9 December 2013; Accepted 18 March 2014; Published 24 April 2014

Academic Editor: Jianming Zhan

Copyright © 2014 Chung-Ta Li 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. M. Biglarbegian, W. W. Melek, and J. M. Mendel, “On the stability of interval type-2 tsk fuzzy logic control systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 40, no. 3, pp. 798–818, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. J. R. Castro, O. Castillo, P. Melin, and A. Rodríguez-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 Scopus
  3. K.-H. Cheng, C.-F. Hsu, C.-M. Lin, T.-T. Lee, and C. Li, “Fuzzy-neural sliding-mode control for DC-DC converters using asymmetric gaussian membership functions,” IEEE Transactions on Industrial Electronics, vol. 54, no. 3, pp. 1528–1536, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. C.-F. Juang, “A hybrid of genetic algorithm and particle swarm optimization for recurrent network design,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 2, pp. 997–1006, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. C.-F. Juang and P.-H. Chang, “Designing fuzzy-rule-based systems using continuous ant-colony optimization,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 138–149, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. C.-F. Juang, C.-M. Hsiao, and C.-H. Hsu, “Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 14–26, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-H. Lee, “Stabilization of nonlinear nonminimum phase systems: adaptive parallel approach using recurrent fuzzy neural network,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 2, pp. 1075–1088, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. C.-H. Lee, C.-T. Li, and F.-Y. Chang, “A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization,” Fuzzy Sets and Systems, vol. 171, pp. 22–43, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  9. C.-H. Lee, F.-K. Chang, C.-T. Kuo, and H.-H. Chang, “A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design,” International Journal of Systems Science, vol. 43, no. 2, pp. 231–247, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  10. 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
  11. C.-H. Lee and Y.-C. Lin, “An adaptive type-2 fuzzy neural controller for nonlinear uncertain systems,” Control and Intelligent Systems, vol. 33, no. 1, pp. 13–25, 2005. View at Google Scholar · View at Scopus
  12. 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 MathSciNet
  13. 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
  14. 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
  15. C.-J. Lin, “A GA-based neural fuzzy system for temperature control,” Fuzzy Sets and Systems, vol. 143, no. 2, pp. 311–333, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  16. C.-J. Lin and W.-H. Ho, “An asymmetry-similarity-measure-based neural fuzzy inference system,” Fuzzy Sets and Systems, vol. 152, no. 3, pp. 535–551, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  17. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, USA, 1996.
  18. F.-J. Lin, R.-J. Wai, and C.-C. Lee, “Fuzzy neural network position controller for ultrasonic motor drive using push-pull DC-DC converter,” IEE Proceedings: Control Theory and Applications, vol. 146, no. 1, pp. 99–107, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. 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. 77–85, 2007. View at Google Scholar · View at Scopus
  20. P. Melin and O. Castillo, “A new method for adaptive control of non-linear plants using Type-2 fuzzy logic and neural networks,” International Journal of General Systems, vol. 33, no. 2-3, pp. 289–304, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Ozen and J. M. Garibaldi, “Effect of type-2 fuzzy membership function shape on modelling variation in human decision making,” in Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 2, pp. 971–976, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. O. Castillo, L. Aguilar, N. Cázarez, and S. Cárdenas, “Systematic design of a stable type-2 fuzzy logic controller,” Applied Soft Computing Journal, vol. 8, no. 3, pp. 1274–1279, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. O. Castillo and P. Melin, “A new approach for plant monitoring using Type-2 fuzzy logic and fractal theory,” International Journal of General Systems, vol. 33, no. 2-3, pp. 305–319, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. O. Castillo and P. Melin, Type-2 Fuzzy Logic: Theory and Applications, Springer, 2008.
  25. H. A. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 524–539, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Hagras, F. Doctor, V. Callaghan, and A. Lopez, “An incremental adaptive life long learning approach for type-2 fuzzy embedded agents in ambient intelligent environments,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 41–55, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Hagras, “Type-2 FLCs: a new generation of fuzzy controllers,” IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 30–43, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. B. Q. Hu and C. K. Kwong, “On type-2 fuzzy sets and their t-norm operations,” Information Sciences, vol. 255, pp. 58–81, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  29. B. Q. Hu and C. Y. Wang, “On type-2 fuzzy relations and interval-valued type-2 fuzzy sets,” Fuzzy Sets and Systems, vol. 236, pp. 1–32, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  30. R. John and S. Coupland, “Type-2 fuzzy logic: a historical view,” IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 57–62, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. N. N. Karnik, J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 6, pp. 643–658, 1999. View at Publisher · View at Google Scholar · View at Scopus
  32. J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper Saddle River, NJ, USA, 2001.
  33. J. M. Mendel and R. I. B. John, “Type-2 fuzzy sets made simple,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 117–127, 2002. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Martínez, O. Castillo, and L. T. Aguilar, “Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms,” Information Sciences, vol. 179, no. 13, pp. 2158–2174, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Sepúlveda, O. Castillo, P. Melin, A. Rodríguez-Díaz, and O. Montiel, “Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic,” Information Sciences, vol. 177, no. 10, pp. 2023–2048, 2007. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Sepúlveda, O. Castillo, P. Melin, and O. Montiel, “An efficient computational method to implement type-2 fuzzy logic in control applications,” in Analysis and Design of Intelligent Systems Using Soft Computing Techniques, pp. 45–52, 2007. View at Google Scholar
  37. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning,” Information Sciences, vol. 8, pp. 199–249, 1975. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  38. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Mass, USA, 1989.
  39. H. Bustince, “Indicator of inclusion grade for interval-valued fuzzy sets: application to approximate reasoning based on interval-valued fuzzy sets,” International Journal of Approximate Reasoning, vol. 23, no. 3, pp. 137–209, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  40. S. I. Birbil and S.-C. Fang, “An electromagnetism-like mechanism for global optimization,” Journal of Global Optimization, vol. 25, no. 3, pp. 263–282, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  41. D. Parrott and X. Li, “Locating and tracking multiple dynamic optima by a particle swarm model using speciation,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 4, pp. 440–458, 2006. View at Publisher · View at Google Scholar · View at Scopus
  42. K.-T. Fang and Y. Wang, Number-Theoretic Methods in Statistics, vol. 51, Chapman & Hall, 1994. View at MathSciNet
  43. Q. Zhang, J. Sun, E. Tsang, and J. Ford, “Hybrid estimation of distribution algorithm for global optimization,” Engineering Computations, vol. 21, no. 1, pp. 91–107, 2004. View at Google Scholar · View at Scopus
  44. K. T. Fang, Y. Wang, and P. M. Bentler, “Some applications of number-theoretic methods in statistics,” Statistical Science, vol. 9, no. 3, pp. 416–428, 1994. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  45. J. Tanomaru and S. Omatu, “Process control by on-line trained neural controllers,” IEEE Transactions on Industrial Electronics, vol. 39, no. 6, pp. 511–521, 1992. View at Publisher · View at Google Scholar · View at Scopus