Skip to main content

An Alternative Backward Fuzzy Rule Interpolation Method

  • Chapter
  • First Online:

Abstract

In real-world fuzzy interpolation applications of interconnected rule bases, situations may arise when certain crucial antecedents are absent from given observations. If such missing antecedents were involved in the subsequent interpolation process, the final conclusion would not be deducible using conventional means.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Z. Huang, Q. Shen, Fuzzy interpolative reasoning via scale and move transformations. IEEE Trans. Fuzzy Syst. 14(2), 340–359 (2006)

    Google Scholar 

  2. Z. Huang, Q. Shen, Fuzzy interpolation and extrapolation: a practical approach. IEEE Trans. Fuzzy Syst. 16(1), 13–28 (2008)

    Google Scholar 

  3. K.W. Wong, D. Tikk, T.D. Gedeon, L.T. Kóczy, Fuzzy rule interpolation for multidimensional input spaces with applications: a case study. IEEE Trans. Fuzzy Syst. 13(6), 809–819 (2005)

    Google Scholar 

  4. A. Gupta, H. Eren, Mathematical modeling and on-line control of hydrocyclones. Proc. Aus. IMM 295(2), 31–41 (1990)

    Google Scholar 

  5. M.H. Rider, The Geological Interpretation of Well Logs, 2nd edn. (Whittles, Caithness, Scotland, 1996)

    Google Scholar 

  6. G. Bontempi, H. Bersini, M. Birattari, The local paradigm for modeling and control: from neuro-fuzzy to lazy learning. Fuzzy Sets Syst. 121(1), 59–72 (2001)

    Google Scholar 

  7. L. Kuncheva, Fuzzy versus nonfuzzy in combining classifiers designed by boosting. IEEE Trans. Fuzzy Syst. 11(6), 729–741 (2003)

    Google Scholar 

  8. S.-M. Chen, Y.-C. Chang, Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans. Fuzzy Syst. 19(4), 729–744 (2011)

    Article  MathSciNet  Google Scholar 

  9. D. Tikk, P. Baranyi, T.D. Gedeon, L. Muresan, Generalization of the rule interpolation method resulting always in acceptable conclusion. Tatra Mt. Math. Publ. 21, 73–91 (2001)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shangzhu Jin .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jin, S., Shen, Q., Peng, J. (2019). An Alternative Backward Fuzzy Rule Interpolation Method. In: Backward Fuzzy Rule Interpolation. Springer, Singapore. https://doi.org/10.1007/978-981-13-1654-8_5

Download citation

Publish with us

Policies and ethics