Shannon entropy for intuitionistic fuzzy information
This work addresses uncertainty modeling in fuzzy systems for researchers in computational intelligence, but it is incremental as it builds on existing fuzzy entropy concepts.
The paper tackled the problem of extending Shannon entropy to intuitionistic fuzzy information by introducing a new formula for distance and similarity, constructing measures for information features, and defining escort fuzzy information, resulting in an entropy that verifies four defining conditions and identifies components like fuzziness and incompleteness.
The paper presents an extension of Shannon fuzzy entropy for intuitionistic fuzzy one. Firstly, we presented a new formula for calculating the distance and similarity of intuitionistic fuzzy information. Then, we constructed measures for information features like score, certainty and uncertainty. Also, a new concept was introduced, namely escort fuzzy information. Then, using the escort fuzzy information, Shannon's formula for intuitionistic fuzzy information was obtained. It should be underlined that Shannon's entropy for intuitionistic fuzzy information verifies the four defining conditions of intuitionistic fuzzy uncertainty. The measures of its two components were also identified: fuzziness (ambiguity) and incompleteness (ignorance).