Backward cloud transformation algorithm based on Kullback Leibler divergence.

ABSTRACT: As a bidirectional cognitive model for dealing with uncertainty, cloud model (CM) are commonly used in application scenarios such as fault diagnosis, system modeling, and evaluation. CM achieves bidirectional conversion between qualitative concepts and quantitative values through forward cloud transformation (FCT) and backward cloud transformation (BCT) algorithms. Among them, the BCT algorithm obtains key parameters that characterize the randomness and fuzziness of concepts through the analysis of quantitative sample data, namely the expectation (Ex), entropy (En), and hyper entropy (He) for CM. However, the existing BCT algorithms adopt an integrated modeling approach, ignoring the impact of data with different distribution characteristics on obtaining key parameters for CM. Therefore, t

SUBMITTER: Xu X 

PROVIDER: S-EPMC12843599 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature