Uncertainty Evaluation via Fuzzy Entropy for Multiple Facts

Abstract

The fuzzy entropy designed for multiple facts selection has been carried out in this work. The entropy for the fuzzy data with respect to a specified fact is designed through a distance measure method. The obtained fuzzy entropy is then applied for the selection from multiple facts. From the relevant fuzzy entropy, it is concluded that data uncertainty information is limited by the total fact of n-1. The bounded calculation of data uncertainty to each fact is proven for multiple facts, and the decision of fuzzy data to the certain fact among multiple facts has been considered with the assistance of fuzzy entropy calculation.

Three-factor Fuzzy Information Channel Model Representing Subjective Judgments Using Entropy that Integrates both Randomness and Fuzziness

“…Hence, the average characteristic value ( ) and randomness-fuzziness entropy ( ) in the three-factor information channel model are formulated using Equations (15) and (16), respectively. ,…”

“…Accordingly, fuzzy entropy is widely applied as an indicator, and the search for an information concept that ascertains the type of uncertainty from a different perspective in this area has been described as extremely important for the further development of information science [7]. Therefore, many FMCDM methods using the fuzzy entropy of De Luca and Termini [10] have been proposed [12][13][14][15][16][17].…”