iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/BFB0028538
Fuzzy identification of unknown systems based on GA | SpringerLink
Skip to main content
Springer Nature Link
Log in

Fuzzy identification of unknown systems based on GA

  • Conference paper
  • First Online:
Simulated Evolution and Learning (SEAL 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

Included in the following conference series:

Abstract

This paper proposes a method which identifies unknown systems using fuzzy-rule based models(fuzzy models) when the input-output pairs of the system are given. It searches fuzzy models by genetic algorithms based on the given input-output pairs. The method finds all parameters of fuzzy models : the number and the position of the fuzzy sets of each input and the rule base- We encode only the fuzzy partitions of inputs into chromosomes, and then generate fuzzy rules from the encoded fuzzy partitions and the given data. We evaluate the performance with 3 functions. The experiments show that the proposed method properly locates the fuzzy sets on the input domains and generates the fuzzy rules approximating the given data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Ralescu, R. Hartani, Some Issues in Fuzzy and Linguistic Modeling. Proceedings of FUZZ-IEEE/IFES '95, pp. 1903–1910, Yokohama, Japan. 1995.

    Google Scholar 

  2. C.L. Karr, E.J. Gentry. Fuzzy Control of pH Using Genetic Algorithms, IEEE Transactions on Fuzzy Systems, vol. 7, No. 1, pp.46–53. 1993.

    Google Scholar 

  3. P. Thrift, Fuzzy Logic Synthesis with Genetic Algorithms, Proceedings of the 4th International Conference on Genetic algorithm, pp.509–513, 1991.

    Google Scholar 

  4. T. Murata, H. Ishibuchi, Adjusting Membership Functions of Fuzzy Classification Rule by Genetic Algorithms. Proceedings of the 4th IEEE International Conference on Fuzzy Systems. pp.1819–1824. 1995.

    Google Scholar 

  5. T. Furuhashi, K. Nakaoka, Y. Uchikawa, An Efficient finding of Fuzzy Rules Using a New Approach to Genetic Bused Machine Learning, Proceedings of the 4th IEEE International Conference on Fuzzy Systems, pp.715–722. 1995.

    Google Scholar 

  6. C.H. Chang, Y.C. Wu. The Genetic Algorithm Tuning Method for Symmetric Membership Functions of Fuzzy Logic Control Systems, l995 International IEEE/IAS Conference on Industrial Automations and Control, pp.421–438, 1995.

    Google Scholar 

  7. J.K. Kim, H. Lee-Kwang, A design and Implementation of the Fuzzy Logic Controller using the Adaptive Probabilities in Genetic Algorithm, Proceedings of KFMS Spring Conference '95, vol. 5. no. 1, pp.202–208, 1995 (in Korean).

    Google Scholar 

  8. J.H. Lee, H. Lee-Kwang, A Study on Automatic Generation of Fuzzy Controller by Generic Algorithm, Proceedings of KFIS Fall Conference '95. pp. 203–310, 1995 (in Korean).

    Google Scholar 

  9. L.X. Wang, J.M. Mendel, Generating fuzzy rules from numcrical data with applications, IEEE Trans. SMC, vol. 22, pp.1414–l427, 1992.

    Google Scholar 

  10. N.K. Alang Rashid, A.S. Heger. Tuning of Fuzzy Logic Controllers by Parameter Estimation Method, Fuzzy Logic and Control, Prentice-Hall, pp.374–391, 1993.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science, KAIST ( Korea Advanced Institute of Science and Technology ), 305-701, Yousung, Taejon, Seoul Korea

    Jee-Hyong Lee & Hyung Lee-Kwang

Authors
  1. Jee-Hyong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyung Lee-Kwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Xin Yao Jong-Hwan Kim Takeshi Furuhashi

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, JH., Lee-Kwang, H. (1997). Fuzzy identification of unknown systems based on GA. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028538

Download citation

  • DOI: https://doi.org/10.1007/BFb0028538

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation