Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 836414, 12 pages
http://dx.doi.org/10.1155/2013/836414
Research Article

Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

Department of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan

Received 24 January 2013; Revised 18 March 2013; Accepted 21 March 2013

Academic Editor: Bo Shen

Copyright © 2013 Zhi-Ren Tsai. 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. H. Dong, Z. Wang, D. W. C. Ho, and H. Gao, “Robust H fuzzy output-feedback control with multiple probabilistic delays and multiple missing measurements,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 4, pp. 712–725, 2010. View at Publisher · View at Google Scholar
  2. H. O. Wang, K. Tanaka, and M. F. Griffin, “An approach to fuzzy control of nonlinear systems: stability and design issues,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 1, pp. 14–23, 1996. View at Google Scholar
  3. L. Wu, X. Su, P. Shi, and J. Qiu, “Model approximation for discrete-time state-delay systems in the TS fuzzy framework,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 366–378, 2011. View at Publisher · View at Google Scholar
  4. C. H. Sun, Y. T. Wang, and C. C. Chang, “Switching T-S fuzzy model-based guaranteed cost control for two-wheeled mobile robots,” International Journal of Innovative Computing, Information and Control, vol. 8, pp. 3015–3028, 2012. View at Google Scholar
  5. C. H. Sun, S. W. Lin, and Y. T. Wang, “Relaxed stabilization conditions for switching T-S fuzzy systems with practical constraints,” International Journal of Innovative Computing, Information and Control, vol. 8, pp. 4133–4145, 2012. View at Google Scholar
  6. X. Su, P. Shi, L. Wu, and Y. D. Song, “A novel control design on discrete-time Takagi-Sugeno fuzzy systems with time-varying delays,” IEEE Trans on Fuzzy Systems, no. 99, 2013. View at Publisher · View at Google Scholar
  7. X. Su, L. Wu, and P. Shi, “Sensor networks with random link failures: distributed filtering for T-S fuzzy systems,” IEEE Transactions on Industrial Informatics, no. 99, 2012. View at Publisher · View at Google Scholar
  8. Z. Wang, Y. Liu, G. Wei, and X. Liu, “A note on control of a class of discrete-time stochastic systems with distributed delays and nonlinear disturbances,” Automatica, vol. 46, no. 3, pp. 543–548, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  9. F. Li and X. Zhang, “Delay-range-dependent robust H filtering for singular LPV systems with time variant delay,” International Journal of Innovative Computing, Information and Control, vol. 9, pp. 339–353, 2013. View at Google Scholar
  10. R. Yang, H. Gao, and P. Shi, “Delay-dependent robust H control for uncertain stochastic time-delay systems,” International Journal of Robust and Nonlinear Control, vol. 20, no. 16, pp. 1852–1865, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  11. R. Yang, P. Shi, G.-P. Liu, and H. Gao, “Network-based feedback control for systems with mixed delays based on quantization and dropout compensation,” Automatica, vol. 47, no. 12, pp. 2805–2809, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  12. X. Su, P. Shi, L. Wu, and S. K. Nguang, “Induced 2 filtering of fuzzy stochastic systems with time-varying delays,” IEEE Transactions on Systems, Man, and Cybernetics B, no. 99, pp. 1–14, 2012. View at Publisher · View at Google Scholar
  13. L. Wu, X. Su, P. Shi, and J. Qiu, “A new approach to stability analysis and stabilization of discrete-time T-S fuzzy time-varying delay systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 41, no. 1, pp. 273–286, 2011. View at Publisher · View at Google Scholar
  14. B. S. Chen, C. S. Tseng, and H. J. Uang, “Mixed H2/H fuzzy output feedback control design for nonlinear dynamic systems: an LMI approach,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 3, pp. 249–265, 2000. View at Publisher · View at Google Scholar
  15. K. Tanaka, “An approach to stability criteria of neural-network control systems,” IEEE Transactions on Neural Networks, vol. 7, no. 3, pp. 629–642, 1996. View at Google Scholar
  16. X. Su, L. Wu, P. Shi, and Y. D. Song, “H model reduction of T-S fuzzy stochastic systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 42, pp. 1574–1585, 2012. View at Publisher · View at Google Scholar
  17. H. T. Siegelmann, B. G. Horne, and C. L. Giles, “Computational capabilities of recurrent NARX neural networks,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 27, no. 2, pp. 208–215, 1997. View at Google Scholar
  18. K. R. Lee, J. H. Kim, E. T. Jeung, and H. B. Park, “Output feedback robust H control of uncertain fuzzy dynamic systems with time-varying delay,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 6, pp. 657–664, 2000. View at Publisher · View at Google Scholar
  19. Y. Y. Cao and P. M. Frank, “Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 2, pp. 200–211, 2000. View at Publisher · View at Google Scholar
  20. G. L. Plett, “Adaptive inverse control of linear and nonlinear systems using dynamic neural networks,” IEEE Transactions on Neural Networks, vol. 14, no. 2, pp. 360–376, 2003. View at Publisher · View at Google Scholar
  21. F. J. Lin, W. J. Hwang, and R. J. Wai, “A supervisory fuzzy neural network control system for tracking periodic inputs,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 1, pp. 41–52, 1999. View at Google Scholar
  22. C. Li and K.-H. Cheng, “Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling,” Fuzzy Sets and Systems, vol. 158, no. 2, pp. 194–212, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  23. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, New York, NY, USA, 1997.
  24. T. Niknam, H. D. Mojarrad, and M. Nayeripour, “A new hybrid fuzzy adaptive particle swarm optimization for non-convex economic dispatch,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 1, pp. 189–202, 2011. View at Google Scholar
  25. Y. C. Ho, W. C. Hsu, and C. C. Chang, “Multi-category and multi-standard project selection with fuzzy value-based time limit,” International Journal of Innovative Computing, Information and Control, vol. 9, pp. 971–989, 2013. View at Google Scholar
  26. C. M. Lin, C. F. Hsu, and R. G. Yeh, “Adaptive fuzzy sliding-mode control system design for brushless DC motors,” International Journal of Innovative Computing, Information and Control, vol. 9, pp. 1259–1270, 2013. View at Google Scholar
  27. H. M. Lee, C. F. Fuh, and J. S. Su, “Fuzzy parallel system reliability analysis based on level (λ, ρ) interval-valued fuzzy numbers,” International Journal of Innovative Computing, Information and Control, vol. 8, pp. 5703–5713, 2012. View at Google Scholar
  28. C. Y. Huang, T-S fuzzy controller design for DC-DC power converter [M.S. thesis], Chung Yuan Christian University, Taiwan, 2002.
  29. J. P. LaSalle, “Some extensions of Liapunov's second method,” vol. 7, pp. 520–527, 1960. View at Google Scholar · View at MathSciNet
  30. W. D. Chang, R. C. Hwang, and J. G. Hsieh, “Stable direct adaptive neural controller of nonlinear systems based on single auro-tuning neuron,” Neurocomputing, vol. 48, pp. 541–554, 2002. View at Publisher · View at Google Scholar