Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Design of IG-based Fuzzy Models Using Improved Space Search Algorithm

개선된 공간 탐색 알고리즘을 이용한 정보입자 기반 퍼지모델 설계

  • Received : 2011.11.19
  • Accepted : 2011.12.16
  • Published : 2011.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

Keywords

References

  1. R.M. Tong, "The evaluation of fuzzy models derived from experimental data" J. Fuzzy Sets Syst. Vol. 13, pp 1-12, 1980.
  2. C.W. Xu, Y. Zailu, "Fuzzy model identification self learning for dynamic system" J. IEEE Trans. on System, Man, and Cybernetics. Vol. 17(4), pp, 683-689, 1987. https://doi.org/10.1109/TSMC.1987.289361
  3. S.K. Oh., W. Pedrycz, "Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems" J. Fuzzy Sets and Syst. Vol 115(2), pp, 205-230, 2000. https://doi.org/10.1016/S0165-0114(98)00174-2
  4. S.K. Oh, W. Pedrycz, K.J Park, "Identification of fuzzy systems by means of genetic optimization and data granulation", Journal of Intelligent & Fuzzy Systems, Vol 18, pp. 31-41, 2007.
  5. W. Huang, L. Ding, S.K. Oh, C.W. Jeong, and S.C. Joo. 2010. Identification of fuzzy inference system based on information granulation. KSII Trans. Internet and Information systems. 4(4), 575-594.
  6. R. Hinterding. Gaussian mutation and self-adaption for numeric genetic algorithms. 1995. IEEE international conference on evolutionary computation. 384-389.
  7. W. Pedrycz, "An identification algorithm in fuzzy relational system". J. Fuzzy Sets Syst. Vol 13, pp, 153-167, 1984. https://doi.org/10.1016/0165-0114(84)90015-0
  8. M. Sugeno, T. Yasukawa, "Linguistic modeling based on numerical data" In: IFSA'91 Brussels, Computer, Management & System Science. pp, 264-267. 1991.
  9. B. J. Park., W. Pedrycz., S. K. Oh, "Identification of Fuzzy Models with the Aid of Evolutionary Data Granulation". IEE Proc.-Control Theory and Applications, Vol. 148, pp, 406-418, 2001. https://doi.org/10.1049/ip-cta:20010677
  10. J.N. Choi, S.K. Oh, and W. Pedrycz, "Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation", International Journal of Approximate Reasoning, Vol 49, pp, 631-648, 2008. https://doi.org/10.1016/j.ijar.2008.06.006
  11. Oh, S.K., Pedrycz, W., and Prak, H.S. 2003. Hybrid identification in fuzzy-neural networks. Fuzzy Set System. 138 ( Sep. 2003), 399-426. https://doi.org/10.1016/S0165-0114(02)00441-4
  12. Park, H.S., and Oh, S.K. 2003. Fuzzy relation-based fuzzy neural-networks using a hybrid identification algorithm. Int. J. Cont., Autom., Syst. 1 (Sep. 2003), 289-300.
  13. Park, H.S., and Oh, S.K. 2003. Multi-FNN identification based on HCM clustering and evolutionary fuzzy granulation. Int. J. Cont., Autom., Syst. 1 (Jun. 2003), 194-202.
  14. Oh, S.K., Pedrycz, W., and Prak, H.S. 2002. Implicit rule-based fuzzy-neural networks using the identification algorithm of hybrid scheme based on information granulation. Adv. Eng.Inform.16(Oct.2002),247-263. https://doi.org/10.1016/S1474-0346(03)00016-8
  15. Oh, S.K., and Pedrycz, W. 2004. A new approach to self-organizing multi-layer fuzzy polynomial neural networks based on genetic optimization. Adv. Eng.Inform.18(Jan.2004),29-39. https://doi.org/10.1016/j.aei.2004.05.001
  16. Pedrycz, W., and Kwak, K.C. "Linguistic models as a framework of user-centric system modeling," IEEE Trans. Syst., man cybern. -PART A: Systems and humans, vol. 36, no. 4, pp. 727-745, 2006. https://doi.org/10.1109/TSMCA.2005.855755
  17. Pedrycz, W., Park, H.S., and S.K. Oh, "A granular-oriented development of functional radial basis function neural networks," Neurocomputing, vol. 72, pp. 420-435, 2008. https://doi.org/10.1016/j.neucom.2007.12.016