International Journal of Control, Automation, and Systems

  • 제6권6호
  • /
  • Pages.928-938
  • /
  • 2008
  • /
  • 1598-6446(pISSN)
  • /
  • 2005-4092(eISSN)
제어로봇시스템학회 (Institute of Control, Robotics and Systems)
권호 표지

An Adaptive Input Data Space Parting Solution to the Synthesis of N euro- Fuzzy Models

  • 발행 : 2008.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

키워드

참고문헌

  1. M. Panella and A. S. Gallo, "An input-output clustering approach to the synthesis of ANFIS networks," IEEE Trans. on fuzzy systems, vol. 13, no. 1, February 2005
  2. A. Rizzi, M. Panella, and F. M. F. Mascioli, "Adaptive resolution min-max classifiers," IEEE Trans. on Neural Networks, vol. 13, no. 2, pp. 402-414, March 2002 https://doi.org/10.1109/72.991426
  3. M. Sugeno and T. Yasukawa, "A fuzzy logic based approach to qualitative modeling," IEEE Trans. on Fuzzy Systems, vol. 1, no. 1, pp. 7-31, Feb. 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  4. S.-J. Lee and C.-S. Ouyang, "A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning," IEEE Trans. on Fuzzy Systems, vol. 11, no. 3, pp. 341-353, June 2003 https://doi.org/10.1109/TFUZZ.2003.812693
  5. Y. Lin, G. A. Cungningham III, and S. V. Coggeshall, "Using fuzzy partitions to create fuzzy system from input-output data and set the initial weights in fuzzy neural network," IEEE Trans. on Fuzzy systems, vol. 5, pp. 614-621, Aug. 1997 https://doi.org/10.1109/91.649913
  6. C. C. Wong and C. C. Chen, "A hybrid clustering and gradient decent approach for fuzzy modeling," IEEE Trans. Syst. Man, Cybern. B, vol. 29, pp. 686-693, Dec. 1999 https://doi.org/10.1109/3477.809024
  7. T. Takagi and M. Sugeno, "Fuzzy identification of systems and applications to modeling and control," IEEE Trans. Syst. Man, Cybern., vol. 15, no. 1, pp. 116-132, Jan. 1985
  8. P. K. Simpson, "Fuzzy min-max neural networks - Part 1: Classification," IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 776-786. 1992 https://doi.org/10.1109/72.159066
  9. P. K. Simpson, "Fuzzy min-max neural networks - Part 2: Clustering," IEEE Trans. on Neural Networks, vol. 1, no. 1, pp. 32-45, 1993
  10. S. D. Nguyen and H. Q. Le, "Adaptive algorithm for training of neural networks based on method conjugate gradient," Journal of Science & Technology, no. 58, pp. 68-73, 2006
  11. J. Mao, J. Zhang, Y. Yue, and H. Ding, "Adaptive-tree-structure-based fuzzy infer-ence system," IEEE Trans. on Fuzzy Systems, vol. 13, no. 1, February 2005