Fibers and Polymers

The Korean Fiber Society (한국섬유공학회)
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Real-time Identification of the Draft System Using Neural Network

  • Chun Soon-Yong (School of IT Electronic Engineering, Dongyang University) ;
  • Bae Han-Jo (School of Textiles, Yeungnam University) ;
  • Kim Seon-Mi (School of Textiles, Yeungnam University) ;
  • Suh Moon-W. (College of Textiles, North Carolina State University) ;
  • Grady P. (College of Textiles, North Carolina State University) ;
  • Lyoo Won-Seok (School of Textiles, Yeungnam University) ;
  • Yoon Won-Sik (School of Textiles, Yeungnam University) ;
  • Han Sung-Soo (School of Textiles, Yeungnam University)
  • Published : 2006.03.01
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Abstract

Making a good model is one of the most important aspects in the field of a control system. If one makes a good model, one is now ready to make a good controller for the system. The focus of this thesis lies on system modeling, the draft system in specific. In modeling for a draft system, one of the most common methods is the 'least-square method'; however, this method can only be applied to linear systems. For this reason, the draft system, which is non-linear and a time-varying system, needs a new method. This thesis proposes a new method (the MLS method) and demonstrates a possible way of modeling even though a system has input noise and system noise. This thesis proved the adaptability and convergence of the MLS method.

Keywords

References

  1. S. S. Han, J. Korean Fiber Soc., 33, 1067 (1996)
  2. Y. Huh, W. S. Kwon, and S. H. Jang, J. Korean Fiber Soc., 37, 171 (2000)
  3. B. Choi and Y. Huh, J. Korean Fiber Soc., 32, 300 (1995)
  4. R. Johansson, 'System Modeling Identification', pp.74-132, Prentice Hall Inc., 1993
  5. A. A. Giordano and F. M. Hsu, 'Least Square Estimation with Applications to Digital Signal Processing', John Wiley, 1985
  6. D. C. Park, Identification of Stationary/Non-stationary System Using Artificial Neural Networks, M.S., 1990
  7. R. Rojas, 'Neural Networks', Springer, 1996
  8. F. H. She, L. X. Kong, S. Nahauandi, and A. Z. Kouzani, Text. Res. J., 72, 594 (2002) https://doi.org/10.1177/004051750207200706
  9. C. C. Huang and K. T. Chang, Text. Res. J., 71, 987 (2001) https://doi.org/10.1177/004051750107101109
  10. D. W. Patterson, 'Introduction to Artificial Intelligence and Expert Systems', Prentice Hall, 1990
  11. N. B. Karayiannis and A. N. Venetsanopoulos, 'Artificial Neural Networks : Learning Algorithms, Performance Evaluation, and Applications', Kluwer Academic, 1993