Skip to main content

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 284))

  • 1357 Accesses

Abstract

In this chapter, new analytical formulae for membership functions of extended t-norms are derived. We consider the following cases: extended minimum, minimum-based extensions of continuous t-norms, extended continuous t-norms based on drastic-product, and extended Ɓukasiewicz t-norm based on continuous Archimedean t-norms. As a dual concept to extended t-norms, extended t-conorms and their formulae are considered. These cases cover almost all practical engineering situations when we implement type-2 fuzzy logic systems. In many cases, we get formulae that preserve shapes, which enable us to derive adaptive network fuzzy inference systems of type- 2. Otherwise, some approximations are needful, or more general notion of a triangular norm on fuzzy truth values (t-norm of type-2 for short) is needed, whose axiomatics we provide briefly. Finally, implications on fuzzy truth values, especially their family called simplicatoins, are derived in order to prepare foundations for structures of uncertain fuzzy logic systems.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baldwin, J.F., Pilsworth, B.W.: Axiomatic approach to implication for approximate reasoning with fuzzy logic. Fuzzy Sets and Systems 3(2), 193–219 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  2. Carlsson, C., Fullér, R.: Fuzzy reasoning in decision making and optimization. Studies in Fuzziness and Soft Computing, vol. 82. Physica-Verlag, Heidelberg (2002)

    Book  MATH  Google Scholar 

  3. Castillo, O., Melin, P.: Type-2 fuzzy logic: theory and applications. Studies in Fuzzines and Soft Computing, vol. 223. Springer (2008)

    Google Scholar 

  4. Choi, B.I., Rhee, F.C.H.: Interval type-2 fuzzy membership function generation methods for pattern recognition. Information Sciences 179, 2102–2122 (2009)

    Article  MATH  Google Scholar 

  5. Dubois, D., Prade, H.: Fuzzy sets and systems: Theory and applications. Academic Press, Inc., New York (1980)

    MATH  Google Scholar 

  6. Dubois, D., Prade, H.: Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions. Fuzzy Sets and Systems 40, 143–202 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  7. Dziech, A., GorzaƂczany, M.B.: Decision making in signal transmission problems with interval-valued fuzzy sets. Fuzzy Sets and Systems 23, 191–203 (1987)

    Article  MathSciNet  Google Scholar 

  8. Fodor, J., Roubens, M.: Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer Academic Publishers, Dordrecht (1994)

    MATH  Google Scholar 

  9. FullĂ©r, R., Keresztfalvi, T.: On generalization of nguyen’s theorem. Fuzzy Sets and Systems 41, 371–374 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  10. FullĂ©r, R., Keresztfalvi, T.: t-norm based addition of fuzzy intervals. Fuzzy Sets and Systems 51, 155–159 (1992)

    Article  MathSciNet  Google Scholar 

  11. Gera, Z.: Fuzzy reasoning models and fuzzy truth value based inference. PhD thesis. University of Szeged (2009)

    Google Scholar 

  12. Gera, Z., Dombi, J.: Type-2 implications on non-interactive fuzzy truth values. Fuzzy Sets and Systems 159(22), 3014–3032 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  13. GorzaƂczany, M.B.: A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Systems 21, 1–17 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hagras, H.A.: A hierarchical type-2 fuzzy logic control architecture for autonomous robots. IEEE Transactions on Fuzzy Systems 12(4), 524–539 (2004)

    Article  Google Scholar 

  15. Hong, D.H., Hwang, S.Y.: On the convergence of t-sum of l–r fuzzy numbers. Fuzzy Sets and Systems 63, 176–180 (1994)

    Article  MathSciNet  Google Scholar 

  16. Karnik, N.N., Mendel, J.M.: Operations on type-2 fuzzy sets. Fuzzy Sets and Systems 122, 327–348 (2000)

    Article  MathSciNet  Google Scholar 

  17. Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems 7(6), 643–658 (1999)

    Article  Google Scholar 

  18. Kawaguchi, M., Miyakoshi, M.: Extended triangular norms in type-2 fuzzy logic. In: EUFIT 1999 7th European Congress on Intelligent Techniques & Soft Computing, Aachen (1999)

    Google Scholar 

  19. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer, Dordrecht (2000)

    MATH  Google Scholar 

  20. Mendel, J.M.: Uncertain rule-based fuzzy logic systems: Introduction and new directions 2001. Prentice Hall PTR, Upper Saddle River (2001)

    MATH  Google Scholar 

  21. Mendel, J.M.: Advances in type-2 fuzzy sets and systems. Information Sciences 177, 84–110 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  22. Mesiar, R.: A note to the t-sum of l–r fuzzy numbers. Fuzzy Sets and Systems 79, 259–261 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  23. Mesiar, R.: Triangular norm-based addition of fuzzy intervals. Fuzzy Sets and Systems 91, 231–237 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  24. Mizumoto, M., Tanaka, K.: Some properties of fuzzy sets of type-2. Information and Control 31, 312–340 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  25. Nguyen, H.T.: A note on the extension principle for fuzzy sets. Journal of Mathematical Analysis and Applications 64, 369–380 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  26. Reiser, R.H.S., Dimuro, G.P., Bedregal, B.C., Santiago, R.H.N., Callejas-Bedregal, R.: Simplications on complete lattices and the interval constructor. TEMA — Tendencias em Matemtica Aplicada e Computacional 9(1), 143–154 (2008)

    MATH  Google Scholar 

  27. Sepulveda, R., Castillo, O., Melin, P., Montiel, O.: An efficient computational method to implement type-2 fuzzy logic in control applications. In: Melin, P., et al. (eds.) Analysis and Design of Intelligent Systems using Soft Computing Techniques, 1st edn., vol. 41, ch. 5, pp. 45–52. Springer, Germany (2007)

    Chapter  Google Scholar 

  28. Starczewski, J., Rutkowski, L.: Neuro-fuzzy systems of type 2. In: Proc. 1st Int’l Conf. on Fuzzy Systems and Knowledge Discovery, Singapore, vol. 2, pp. 458–462 (2002)

    Google Scholar 

  29. Starczewski, J.T.: Extended triangular norms on gaussian fuzzy sets. In: Proc. EUSFLAT-LFA 2005 Conf., Barcelona, Spain, pp. 872–877 (2005)

    Google Scholar 

  30. Starczewski, J.T.: A triangular type-2 fuzzy logic system. In: Proc. IEEE-FUZZ 2006, Vancouver CA, pp. 7231–7238 (2006)

    Google Scholar 

  31. Starczewski, J.T.: Efficient triangular type-2 fuzzy logic systems. International Journal of Approximate Reasoning 50, 799–811 (2009a)

    Article  MATH  Google Scholar 

  32. Starczewski, J.T.: Extended triangular norms. Information Sciences 179, 742–757 (2009b)

    Article  MathSciNet  MATH  Google Scholar 

  33. Tahayori, H., Tettamanzi, A., Degli Antoni, G., Visconti, A.: On the calculation of extended max and min operations between convex fuzzy sets of the real line. Fuzzy Sets and Systems 160(21), 3103–3114 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  34. Uncu, O., TĂŒrkƟen, I.B.: Discrete interval type 2 fuzzy system models using uncertainty in learning parameters. IEEE Transactions on Fuzzy Systems 15(1), 90–106 (2007)

    Article  Google Scholar 

  35. Walker, C.L., Walker, E.A.: The algebra of fuzzy truth values. Fuzzy Sets and Systems 2, 309–347 (2005)

    Article  Google Scholar 

  36. Walker, C.L., Walker, E.A.: Sets with type-2 operations. International Journal of Approximate Reasoning 50, 63–71 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  37. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning — I. Information Sciences 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhou, S., Garibaldi, J., John, R., Chiclana, F.: On constructing parsimonious type-2 fuzzy logic systems via influential rule selection. IEEE Transactions on Fuzzy Systems 17(3), 654–667 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Janusz T. Starczewski .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Starczewski, J.T. (2013). Algebraic Operations on Fuzzy Valued Fuzzy Sets. In: Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Studies in Fuzziness and Soft Computing, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29520-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29520-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29519-5

  • Online ISBN: 978-3-642-29520-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics