LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Singh Khuman, Arjab; Yang, Yingjie; John, Robert; Liu, Sifeng
Languages: English
Types: Conference object
Subjects: R-Fuzzy Sets, Grey Whitenisation Weight Function, Significance Measure, Grey System Theory, Grey Analysis
This paper investigates the use of grey theory to en- hance the concept of an R-fuzzy set, with regards to the precision of the encapsulating set of returned significance values. The use of lower and upper approximations from rough set theory, allow for an R-fuzzy approach to encapsulate uncertain fuzzy membership values; both collectively generic and individually specific. The authors have previously created a significance measure, which when combined with an R-fuzzy set provides one with a refined approach for expressing complex uncertainty. This pairing of an R-fuzzy set and the significance measure, replicates in part, the high detail of uncertainty representation from a type-2 fuzzy approach, with the relative ease and objectiveness of a type-1 fuzzy approach. As a result, this new research method allows for a practical means for domains where ideally a generalised type-2 fuzzy set is more favourable, but ultimately unfeasible due to the subjectiveness of type-2 fuzzy membership values. This paper focuses on providing a more effective means for the creation of the set which encapsulates the returned degrees of significance. Using grey techniques, rather than the arbitrary configuration of the original work, the result is a high precision set for encapsulation, with the minimal configuration of parameter values. A worked example is used to demonstrate the effectiveness of using grey theory in conjunction with R-fuzzy sets and the significance measure.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Y. Yang and C. Hinde, “A new extension of fuzzy sets using rough sets: R-fuzzy sets,” Information Sciences, vol. 180, no. 3, pp. 354 - 365, 2010.
    • [2] A. S. Khuman, Y. Yang, and R. John, “A significance measure for rfuzzy sets,” in Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on, Aug 2015, pp. 1-6.
    • [3] A. Khuman, Y. Yang, and R. John, “Quantification of r-fuzzy sets,” Expert Systems with Applications, vol. 55, pp. 374 - 387, 2016.
    • [4] A. S. Khuman, Y. Yang, and S. Liu, “Grey relational analysis and natural language processing,” in 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS), Aug 2015, pp. 107-112.
    • [5] L. Sifeng, J. Forrest, and Y. Yingjie, “A brief introduction to grey systems theory,” in Grey Systems and Intelligent Services (GSIS), 2011 IEEE International Conference on, Sept 2011, pp. 1-9.
    • [6] Z. Pawlak, “Rough set theory and its applications to data analysis,” Cybernetics & Systems, vol. 29, no. 7, pp. 661-688, 1998.
    • [7] S. Liu and Y. Lin, Grey Information: Theory and Applications, 1st ed., ser. Advanced Information and Knowledge Processing. Springer-Verlag London, 2006.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article