Study of an Analytical Approach using Information Field-based Fuzzy Entropy

Abstract

As the large number of digital devices used in our daily life, great myriad of data will be produced and how to analyze such data brings great challenge in examining the information such as information degree/measurement. Entropy is one of possible ways to analyze the information which is fuzzy and random. In order to analyze the entropy in a more precise way, this paper presents an analytical approach which uses information field-based fuzzy entropy to define the distance of information transferring function and expected information. Using the cross information and information transferring theory, this approach extends the single information source to multi-information sets adopting the fuzzy theory. Based on this approach, it is observed that, the information field entropy not only includes the independent fuzzy entropy and Shannon entropy, but also includes the cross-part. That reveals a fuzzy variable which has two independent parts: relative independent fuzziness and randomicity as well as the combanability. Additionally, when the fuzziness disappears, the information field-based entropy will be degenerated to Shannon Entropy. While, when the randomicity is getting weak, the information fieldbased entropy will be degenerated to fuzzy entropy.