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A novel optimization method for belief rule base expert system with activation rate.

Gang Xiang1,2, Jie Wang3, XiaoXia Han4

  • 1Beihang University School of Automation Science and Electrical Engineering, Beijing, 100191, China.

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A new optimization method for belief rule base (BRB) expert systems prunes inadequately activated rules, solving combinatorial explosion and preserving expert interpretability for complex systems.

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Area of Science:

  • Artificial Intelligence
  • Expert Systems
  • Computational Intelligence

Background:

  • Belief Rule Base (BRB) expert systems offer advantages in handling semi-quantitative information and uncertainty.
  • However, BRB systems face challenges with combinatorial explosion, consuming significant resources and potentially reducing interpretability.

Purpose of the Study:

  • To propose a novel optimization method for BRB expert systems.
  • To address the combinatorial explosion problem and maintain parameter interpretability.

Main Methods:

  • A new optimization approach using an activation rate to prune unactivated or inadequately activated rules.
  • Application of the activation rate for parallel optimization by dividing training datasets.

Main Results:

  • The proposed method effectively resolves the combinatorial explosion issue in BRB optimization.
  • It enables full utilization of quantitative data while preserving expert-defined parameter interpretability.
  • Case studies demonstrate the method's effectiveness and advantages.

Conclusions:

  • The novel optimization method significantly enhances the efficiency and applicability of BRB expert systems.
  • It overcomes computational limitations, making BRBs more suitable for real-time complex systems.
  • This research expands the application scope of BRB expert systems.