Backward cloud transformation algorithm based on Kullback Leibler divergence
Xiaobin Xu, Kangwei Yu, Junhe Fu, Lingjun Dong, Haohao Guo, Hong He, Lu Zhang
TL;DR
This paper introduces a new backward cloud transformation algorithm using KL divergence to improve the accuracy of cloud model parameters in handling uncertainty.
Contribution
A novel BCT algorithm using KL divergence to better capture distribution differences in sample data for cloud model parameter estimation.
Findings
The proposed BCT algorithm outperforms traditional methods in parameter accuracy.
KL divergence effectively evaluates atomization states in cloud model transformation.
Experimental results on UCI and fault diagnosis data validate improved performance.
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, this paper proposes a BCT algorithm based on Kullback Leibler (KL) divergence, aiming to refine the…
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Taxonomy
TopicsAdvanced Decision-Making Techniques · Cognitive Science and Mapping · Solar Radiation and Photovoltaics