Advertisement

Selection of input variables of fuzzy model using genetic algorithm with quick fuzzy inference

  • S. Matsushita
  • T. Furuhashi
  • H. Tsutsui
  • Y. Uchikawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1285)

Abstract

Hierarchical fuzzy modeling using fuzzy neural networks (FNN) is one of the effective approaches for modeling of nonlinear systems. Decision of antecedent structures of fuzzy models of nonlinear systems is made possible by a combination of FNN and genetic algorithm (GA). The disadvantage of this fuzzy modeling method is that the learning of FNN is time consuming. This paper presents an efficient fuzzy modeling method using simple fuzzy inference. The results of fuzzy modeling are heavily dependent on evaluation criteria. This paper also studies effects of evaluation criteria for the decision of the antecedent structure. Numerical experiments are done.

Keywords

Fuzzy Modeling Fuzzy Neural Network Nonlinear Modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    S Horikawa, T. Furuhashi, Y Uchikawa and T. Tagawa, “A Study on Fuzzy Modeling Using Fuzzy Neural Networks”, Proc. of the Int'l. Fuzzy Eng. Symp.(IFES'91), pp. 562–573, 1991Google Scholar
  2. [2]
    S.Horikawa, T.Furuhashi, YUchikawa, “On Fuzzy Modeling Using Fuzzy Neural Networks with the Back-Propagation Algorithm,” IEEE Trans. on Neural Networks, vol.3, no.5, pp.801–806, 1992Google Scholar
  3. [3]
    S.Nakayama, T.Furuhashi, and YUchikawa, “A Proposal of Hierarchical Fuzzy Modeling Method”, Journal of Japan Society for Fuzzy Theory and Systems (in Japanese), vol.5, no.5, pp. 1155–1168, 1993Google Scholar
  4. [4]
    S.Matsushita, A.Kuromiya, M.Yamaoka, T.Furuhashi, and YUchikawa, “Hierarchical Fuzzy Modeling Method with multiple submodels”, Journal of Japan Society for Fuzzy Theory and Systems (in Japanese), vol.8, no.3, pp.525–528, 1996Google Scholar
  5. [5]
    S.Matsushita, A.Kuromiya, M.Yamaoka, T.Furuhashi, and YUchikawa, “A Sturdy on Fuzzy GMDH”, 10th Fuzzy System Symposium ( in Japanese), pp. 383–386, 1994Google Scholar
  6. [6]
    S.Matsushita, A.Kuromiya, M.Yamaoka, TFuruhashi, and YUchikawa, “A Study on Fuzzy GMDH with Comprehensible Fuzzy Rules”, Proc. of Seiken/IEEE Sympo. on Emerging Technologies & Factory Automation (ETFA'94), pp. 192–198, 1994Google Scholar
  7. [7]
    K. Nagasaka, H. Ichihashi, “Neuro-Fuzzy GMDH and Its Application to Modeling of Grinding Characteristics”, 9th Fuzzy System Symposium (in Japanese), pp. 449–452, 1993Google Scholar
  8. [8]
    H. Tanaka, K. Yokode, H. Ishibuchi, “GMDH by Fuzzy If-Then Rules”, 9th Fuzzy System Symposium (in Japanese), pp. 237–240, 1993Google Scholar
  9. [9]
    K. Shimojima, T. Fukuda, Y. Hasegawa, “Self-Tuning Fuzzy Modeling with Adaptive Membership Function, Rules, and Hierarchical Structure Based on Genetic Algorithm”, Fuzzy Sets and Systems, vol.71, no.3, pp. 295–309, 1995Google Scholar
  10. [10]
    S. Matsushita, A. Kuromiya, M. Yamaoka, T.Furuhashi, and YUchikawa, “Determination of Antecedent Structure for Fuzzv Modeling Using Genetic Algorithm”, (ICEC96), pp. 235–238, 1996Google Scholar
  11. [11]
    H.Tsutsui, and Kurosaki, “Fuzzy Topological Case Based Modeling and applications”, 9th Fuzzy System Symposium (in Japanese), pp. 249–252, 1993Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • S. Matsushita
    • 1
  • T. Furuhashi
    • 2
  • H. Tsutsui
    • 3
  • Y. Uchikawa
    • 2
  1. 1.Nagoya Municipal Industrial Research InstituteJapan
  2. 2.Dept. of Information ElectronicsNagoya UniversityJapan
  3. 3.Yamatake-Honeywell Co.Japan

Personalised recommendations