Skip to main content

Abstract

In this paper, we present a methodology to deal with fuzzy reasoning based on the matching function S. The singlc-input-single-output (SISO) fuzzy reasoning scheme and the multi-input-single-output (MISO) fuzzy reasoning scheme are discussed in details.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Z. Cao, A. Kandel, and L. Li, “A new model of fuzzy reasoning,” Fuzzy Sets and Systems, vol. 36, pp. 311–325, 1990.

    Article  MATH  Google Scholar 

  2. T. C. Chang, K. Hasegawa, and C. W. Ibbs, “The effects of membership function on fuzzy reasoning,” Fuzzy Sets and Systems, vol. 44, pp. 169–186, 1991.

    Article  MATH  Google Scholar 

  3. S. M. Chen, “A new approach to handling fuzzy decisionmaking problems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 18, no. 6, pp. 1012–1016, 1988.

    Article  MATH  Google Scholar 

  4. S. M. Chen, J. S. Ke, and J. F. Chang, “Knowledge representation using fuzzy Petri nets,” IEEE Transactions on Knowledge and Data Engineering, vol. 2, no. 3, pp. 311–319, 1990.

    Article  Google Scholar 

  5. Y. Ezawa and A. Kandel, “Robust fuzzy inference,” International Journal of Intelligent Systems, vol. 6, pp. 185–197, 1991.

    Article  MATH  Google Scholar 

  6. J. Giarratano and G. Rilly, Expert Systems: Principles and Programming, Boston: PWS-KENT Publishing Company, 1989.

    Google Scholar 

  7. J. S. Ke and G. T. Her, “A fuzzy information retrieval system model,” Proceedings of 1983 National Computer Symposium, Taiwan, 1983, pp. 147–155.

    Google Scholar 

  8. A. Kandel, Fuzzy Mathematical Techniques with Applications. Addision-Wesley Publishing Company, 1986.

    Google Scholar 

  9. C. C. Lee, “Fuzzy logic in control systems: fuzzy logic controller-part I,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, no. 2, pp. 404–418, 1990.

    Article  MATH  Google Scholar 

  10. M. Mizumoto and H. J. Zimmermann, “Comparison of fuzzy reasoning methods,” Fuzzy Sets and Systems, vol. 8, pp. 253–283, 1982.

    Article  MATH  Google Scholar 

  11. K. J. Schmucker, Fuzzy Sets, Natural Language Computations, and Risk Analysis, Rockville, MD: Computer Science Press, 1984.

    MATH  Google Scholar 

  12. I. B. Turksen and Z. Zhong, “An approximate reasoning approach based on similarity measures,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 18, no. 6, pp. 1049–1056, 1988.

    Article  Google Scholar 

  13. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 328–353, 1965.

    Article  Google Scholar 

  14. L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision process,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, no. 1, pp. 28–44, 1973.

    MATH  Google Scholar 

  15. L. A. Zadeh, “The concepts of a linguistic variable and its application to approximate reasoning (I),” Information Science, vol. 8, pp. 199–249, 1975.

    Article  Google Scholar 

  16. R. Zwick, E. Carlstein, and D. V. Budescu, “Measures of similarity among fuzzy concepts: a comparative analysis,” International Journal of Approximate Reasoning, vol. 1, pp. 221–242, 1987.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 1994 Kluwer Academic Publishers

About this chapter

Cite this chapter

Chen, SM. (1994). A Fuzzy Reasoning Methodology for Rule-Based Systems. In: Fuzzy Reasoning in Information, Decision and Control Systems. International Series on Microprocessor-Based and Intelligent Systems Engineering, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-0-585-34652-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-0-585-34652-6_14

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-7923-2643-4

  • Online ISBN: 978-0-585-34652-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics