Skip to main content
Log in

Type-2 fuzzy variables and their arithmetic

  • Original Paper
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper proposes an axiomatic framework from which we develop the theory of type-2 (T2) fuzziness, called fuzzy possibility theory. First, we introduce the concept of a fuzzy possibility measure in a fuzzy possibility space (FPS). The fuzzy possibility measure takes on regular fuzzy variable (RFV) values, so it generalizes the scalar possibility measure in the literature. One of the interesting consequences of the FPS is that it leads to a new definition of T2 fuzzy set on the Euclidean space \(\Re^m,\) which we call T2 fuzzy vector, as a map to the space instead of on the space. More precisely, we define a T2 fuzzy vector as a measurable map from an FPS to the space \(\Re^m\) of real vectors. In the current development, we are suggesting that T2 fuzzy vector is a more appropriate definition for a T2 fuzzy set on \(\Re^m.\) In the literature, a T2 fuzzy set is usually defined via its T2 membership function, whereas in this paper, we obtain the T2 possibility distribution function as the transformation of a fuzzy possibility measure from a universe to the space \(\Re^m\) via T2 fuzzy vector. Second, we develop the product fuzzy possibility theory. In this part, we give a general extension theorem about product fuzzy possibility measure from a class of measurable atom-rectangles to a product ample field, and discuss the relationship between a T2 fuzzy vector and T2 fuzzy variables. We also prove two useful theorems about the existence of an FPS and a T2 fuzzy vector based on the information from a finite number of RFV-valued maps. The two results provide the possible interpretations for the concepts of the FPS and the T2 fuzzy vector, and thus reinforce the credibility of the approach developed in this paper. Finally, we deal with the arithmetic of T2 fuzzy variables in fuzzy possibility theory. We divide our discussion into two cases according to whether T2 fuzzy variables are defined on single FPS or on different FPSs, and obtain two theorems about T2 fuzzy arithmetic.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Dubois D, Prade H (1979) Operations in a fuzzy-valued logic. Inf Control 43(2):224–240

    Article  MATH  MathSciNet  Google Scholar 

  • Dugundji J (1966) Topology. Allyn and Bacon, Boston

    MATH  Google Scholar 

  • John RI (1998) Type-2 fuzzy sets: an appraisal of theory and applications. Int J Uncertain Fuzziness Knowl Based Syst 6(6):563–576

    Article  MATH  Google Scholar 

  • John RI, Innocent PR, Barnes MR (2000) Neuro-fuzzy clustering of radiograpinc tibia image data using type 2 fuzzy sets. Inf Sci 125(1–4):65–82

    Article  MATH  Google Scholar 

  • Karnik NN, Mendel JM (2001) Centroid of a type-2 fuzzy set. Inf Sci 132(1–4):195–220

    Article  MATH  MathSciNet  Google Scholar 

  • Karnik NN, Mendel JM, Liang Q (1999) Type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 7(6):643–658

    Article  Google Scholar 

  • Klement EP, Mesiar R, Pap E (2000) Triangular Norms. Kluwer, Dordrecht

  • Klir GJ (1999) On fuzzy-set interpretation of possibility theory. Fuzzy Sets Syst 108(3):263–273

    Article  MATH  MathSciNet  Google Scholar 

  • Liang Q, Mendel JM (2000) Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters. IEEE Trans Fuzzy Syst 8(5):551–563

    Article  Google Scholar 

  • Liang Q, Karnik NN, Mendel JM (2000) Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems. IEEE Trans Syst Man Cybern Part C-Appl Rev 30(3):329–339

    Article  Google Scholar 

  • Liu YK (2005) Fuzzy programming with recourse. Int J Uncertain Fuzziness Knowl Based Syst 13(4):381–413

    Article  MATH  Google Scholar 

  • Liu YK (2006) Convergent results about the use of fuzzy simulation in fuzzy optimization problems. IEEE Trans Fuzzy Syst 14(2):295–304

    Article  Google Scholar 

  • Liu YK, Gao J (2007) The independence of fuzzy variables with applications to fuzzy random optimization. Int J Uncertain Fuzziness Knowl Based Syst 15(Suppl 2):1–20

    Article  MATH  MathSciNet  Google Scholar 

  • Liu YK, Liu B, Chen Y (2006) The infinite dimensional product possibility space and its applications. Lect Notes Artif Intell 4114:984–989

    Google Scholar 

  • Mendel JM (2001) Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  • Mendel JM, John RI (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  • Mitchell HB (2005) Pattern recognition using type-II fuzzy sets. Inf Sci 170(2–4):409–418

    Article  Google Scholar 

  • Mizumoto M, Tanaka K (1976) Some properties of fuzzy sets of type-2. Inf Control 31(4):312–340

    Article  MATH  MathSciNet  Google Scholar 

  • Nahmias S (1978) Fuzzy variables. Fuzzy Sets Syst 1(1):97–110

    Article  MATH  MathSciNet  Google Scholar 

  • Nieminen J (1977) Algebraic structure of fuzzy sets of type-2. Kybernetica 13(4):261–273

    MATH  MathSciNet  Google Scholar 

  • Wang P (1982) Fuzzy contactability and fuzzy variables. Fuzzy Sets Syst 8(1):81–92

    Article  MATH  Google Scholar 

  • Yager RR (1980) Fuzzy subsets of type-II in decisions. J Cybern 10(1–3):137–159

    Article  MathSciNet  Google Scholar 

  • Zadeh LA (1975) Concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249

    Article  MathSciNet  Google Scholar 

  • Zadeh LA (1978) Fuzzy set as a basis for a theory of possibility. Fuzzy Sets Syst 1(1):3–28

    Article  MATH  MathSciNet  Google Scholar 

  • Zeng J, Liu ZQ (2006) Type-2 fuzzy hidden Markov models and their application to speech recognition. IEEE Trans Fuzzy Syst 14(3):454–467

    Article  MathSciNet  Google Scholar 

  • Zeng J, Liu ZQ (2007) Type-2 fuzzy sets for pattern recognition: the state-of-the-art. J Uncertain Syst 1(3):163–177

    MathSciNet  Google Scholar 

  • Zhou J, Liu B (2004) Analysis and algorithms of bifuzzy systems. Int J Uncertain Fuzziness Knowl Based Syst 12(3):357–376

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors wish to thank the reviewers for their excellent suggestions that have been incorporated into this paper. This work is supported by the Natural Science Foundation of Hebei Province (A2008000563), the Program for One Hundred Excellent and Innovative Talents in Colleges and Universities of Hebei Province, the National Natural Science Foundation of China, and the CityUHK SRG 7001794, 7001679, and 9041147.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yan-Kui Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, ZQ., Liu, YK. Type-2 fuzzy variables and their arithmetic. Soft Comput 14, 729–747 (2010). https://doi.org/10.1007/s00500-009-0461-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-009-0461-x

Keywords

Navigation