Skip to main content
SpringerLink
Log in

A Self-Organized Fuzzy Neural Network Approach for Rule Generation of Fuzzy Logic Systems

  • Conference paper
Emerging Intelligent Computing Technology and Applications (ICIC 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 375))

Included in the following conference series:

Abstract

This paper shows an algorithm for creating fuzzy logic systems from data by synchronizing its fuzzy sets and rules using a novel neuro fuzzy approach to generate rules and fuzzy sets from analyzing input data. A volatile time series example is solved and analyzed using the residuals of the model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Juang, C.F., Tsao, Y.W.: A Type-2 self-organizing neural fuzzy system and its FPGA implementation. IEEE Trans. Systems, man, and cybernetics. Part B, Cybernetics 38, 1537–1548 (2008)

    Article  Google Scholar 

  2. Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall (2000)

    Google Scholar 

  3. Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall (1992)

    Google Scholar 

  4. Juang, C., Lin, C.: An on-line self-constructing neural fuzzy inference network and its application. IEEE Trans. on Fuzzy Systems 6(1), 12–32 (1998)

    Article  Google Scholar 

  5. Law, A., Kelton, D.: Simulation System and Analysis. Mc Graw Hill International (2000)

    Google Scholar 

  6. Wang, L.X., Mendel, J.M.: Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: Proceedings of the FUZZ-IEEE, vol. 8, pp. 1409–1418. IEEE (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan C. Figueroa-García, Cynthia Ochoa-Rey & Jose Avellaneda-González

Authors
  1. Juan C. Figueroa-García
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cynthia Ochoa-Rey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose Avellaneda-González
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208 016, Kanpur, India

    Phalguni Gupta

  3. Tsinghua University, Beijing, China

    Ling Wang

  4. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    Michael Gromiha

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Figueroa-García, J.C., Ochoa-Rey, C., Avellaneda-González, J. (2013). A Self-Organized Fuzzy Neural Network Approach for Rule Generation of Fuzzy Logic Systems. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39678-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39677-9

  • Online ISBN: 978-3-642-39678-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation