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Backward cloud transformation algorithm based on Kullback Leibler divergence.

Xiaobin Xu1, Kangwei Yu2, Junhe Fu3

  • 1China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing, Hangzhou Dianzi University, Hangzhou, China.

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|January 27, 2026
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Summary
This summary is machine-generated.

This study introduces a new backward cloud transformation (BCT) algorithm using Kullback Leibler (KL) divergence to improve cloud model (CM) parameter accuracy. The method refines expectation, entropy, and hyper entropy estimation by analyzing data distribution, outperforming traditional algorithms.

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Area of Science:

  • Artificial Intelligence
  • Data Science

Background:

  • Cloud Models (CM) are bidirectional cognitive tools for uncertainty, used in fault diagnosis and system modeling.
  • Backward Cloud Transformation (BCT) algorithms extract CM parameters (Ex, En, He) from quantitative data.
  • Existing BCT methods overlook data distribution's impact on parameter accuracy.

Purpose of the Study:

  • To propose a novel BCT algorithm utilizing Kullback Leibler (KL) divergence for enhanced CM parameter estimation.
  • To address limitations of integrated modeling in traditional BCT by considering data distribution characteristics.
  • To refine the accuracy of expectation (Ex), entropy (En), and hyper entropy (He) for cloud models.

Main Methods:

  • Developed a BCT algorithm incorporating KL divergence to analyze sample data distribution.
  • Introduced an atomization template dataset (ATD) derived from coarse-grained data analysis.
  • Implemented differentiated BCT strategies based on KL divergence evaluation of atomization states.

Main Results:

  • The proposed KL divergence-based BCT algorithm demonstrated superior accuracy in obtaining CM key parameters.
  • Comparative analysis using UCI benchmark and real fault diagnosis data validated the method's effectiveness.
  • The approach successfully refines parameter estimation by accounting for varying data distributions.

Conclusions:

  • The novel BCT algorithm based on KL divergence offers a more accurate method for cloud model parameter extraction.
  • This approach enhances the reliability of CM applications in areas like fault diagnosis and system modeling.
  • Considering data distribution characteristics is crucial for improving backward cloud transformation algorithms.