International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Dynamic System Identification Using a Recurrent Compensatory Fuzzy Neural Network

  • Lee, Chi-Yung (Dept. of Computer Science and Information Engineering, Nankai Institute of Technology) ;
  • Lin, Cheng-Jian (Dept. of Computer Science and Engineering, National Chin-Yi University of Technology) ;
  • Chen, Cheng-Hung (Dept. of Electrical and Control Engineering, National Chiao-Tung University) ;
  • Chang, Chun-Lung (Mechanical and Systems Research Laboratories, Industrial Technology Research Institute)
  • Published : 2008.10.31
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Abstract

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.

Keywords

References

  1. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System, Prentice-Hall, NJ, 1996
  2. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993 https://doi.org/10.1109/21.256541
  3. C. J. Lin and C. T. Lin, "Reinforcement learning for an ART-based fuzzy adaptive learning control network," IEEE Trans. Neural Networks, vol. 7, pp. 709-731, June 1996 https://doi.org/10.1109/72.501728
  4. C. F. Juang and C. T. Lin, "An on-line selfconstructing neural fuzzy inference network and its applications," IEEE Trans. on Fuzzy Systems, vol. 6, no. 1, pp. 12-31, Feb. 1998 https://doi.org/10.1109/91.660805
  5. F. J. Lin, C. H. Lin, and P. H. Shen, "Selfconstructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive," IEEE Trans. on Fuzzy Systems, vol. 9, pp. 751-759, Oct. 2001 https://doi.org/10.1109/91.963761
  6. K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. on Neural Networks, vol. 1, pp. 4-27, 1990 https://doi.org/10.1109/72.80202
  7. C. F. Juang and C. T. Lin, "A recurrent selforganizing neural fuzzy inference network," IEEE Trans. on Neural Networks, vol. 10, no. 4, pp. 828-845, July 1999 https://doi.org/10.1109/72.774232
  8. F. J. Lin and R. J. Wai, "Hybrid control using recurrent fuzzy neural network for linearinduction motor servo drive," IEEE Trans. on Fuzzy Systems, vol. 9, no. 1, pp. 102-115, Feb. 2001 https://doi.org/10.1109/91.917118
  9. C. F. Juang, "A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms," IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 155-170, Apr. 2002 https://doi.org/10.1109/91.995118
  10. H. J. Zimmermann and P. Zysno, "Latent connective in human decision," Fuzzy Sets and Systems, vol. 4, pp. 37-51, 1980 https://doi.org/10.1016/0165-0114(80)90062-7
  11. Y. Q. Zhang and A. Kandel, "Compensatory neurofuzzy systems with fast learning algorithms," IEEE Trans. on Neural Networks, vol. 9, no. 1, pp. 83-105, Jan. 1998 https://doi.org/10.1109/72.655032
  12. M. Mizumoto, "Pictorial representations of fuzzy connectives, part II: Cases of compensatory operators and self-dual operators," Fuzzy Sets and Systems, vol. 32, pp. 45-79, 1989 https://doi.org/10.1016/0165-0114(89)90087-0
  13. H. Seker, D. E. Evans, N. Aydin, and E. Yazgan, "Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral doppler ultrasound waveforms," IEEE Trans. on Information Technology in Biomedicine, vol. 5, no. 3, pp. 187-194, 2001 https://doi.org/10.1109/4233.945289
  14. C. J. Lin and C. H. Chen, "Nonlinear system control using compensatory neuro-fuzzy networks," IEICE Fundamentals on Electronics, Communications and Computer Sciences, vol. E86-A, no. 9, pp. 2309-2316, 2003
  15. C. J. Lin and W. H. Ho, "A pseudo-gaussianbased compensatory neural fuzzy system," Proc. of the IEEE International Conference on Fuzzy Systems, St. Louis, MO, USA, pp. 214-220, May 25-28, 2003
  16. C. J. Lin and C. C. Chin, "Prediction and identification using wavelet-based recurrent fuzzy neural networks," IEEE Trans. on Systems, Man, and Cybernetics, vol. 34, no. 5, pp. 2144-2154, Oct. 2004 https://doi.org/10.1109/TSMCB.2004.833330
  17. J. H. Kim and U. Y. Huh, "Fuzzy model based predictive control," Proc. of IEEE Int. Conf. Fuzzy Systems, vol. 1, Anchorage, AK, pp. 405- 409, May 1998
  18. G. Chen, Y. Chen, and H. Ogmen, "Identifying chaotic systems via a wiener-type cascade model," IEEE Control Systems Magazine, vol. 17, no. 5, pp. 29-36, Oct. 1997