Journal
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
Volume 16, Issue 1, Pages -Publisher
CCC PUBL-AGORA UNIV
DOI: 10.15837/ijccc.2021.1.4106
Keywords
fuzzy sets; membership function; information volume; higher-order information volume; entropy
Funding
- National Natural Science Foundation of China [61973332]
Ask authors/readers for more resources
The paper introduces a new method to calculate the information volume of fuzzy membership function, which includes both first-order and higher-order information volume. The efficiency of the proposed method is illustrated through several examples.
Fuzzy membership function plays an important role in fuzzy set theory. However, how to measure the information volume of fuzzy membership function is still an open issue. The existing methods to determine the uncertainty of fuzzy membership function only measure the first-order information volume, but do not take higher-order information volume into consideration. To address this issue, a new information volume of fuzzy membership function is presented in this paper, which includes the first-order and the higher-order information volume. By continuously separating the hesitancy degree until convergence, the information volume of the fuzzy membership function can be calculated. In addition, when the hesitancy degree of a fuzzy membership function equals to zero, the information volume of this special fuzzy membership function is identical to Shannon entropy. Two typical fuzzy sets, namely classic fuzzy sets and intuitiontistic fuzzy sets, are studied. Several examples are illustrated to show the efficiency of the proposed information volume of fuzzy membership function.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available