Disjunctive Cloud Fuzzy Belief Rules for Recognition with Adaptive Parameters

LI Shuang-ming; GUAN Xin; WANG Hai-bin

doi:10.12263/DZXB.20201253

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Disjunctive Cloud Fuzzy Belief Rules for Recognition with Adaptive Parameters

PAPERS | 更新时间：2025-12-08

- Disjunctive Cloud Fuzzy Belief Rules for Recognition with Adaptive Parameters
- ACTA ELECTRONICA SINICA Vol. 50, Issue 2, Pages: 396-403(2022)
- 作者机构：
  
  1.海军航空大学, 山东烟台 264001
  2.92941部队, 辽宁葫芦岛 125001
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201253
  CLC： TP391;TN957;TP274
- Received：06 November 2020，
  
  Revised：2021-01-26，
  
  Published：25 February 2022
- 稿件说明：
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李双明,关欣,王海滨.参数自适应的析取云模糊置信规则识别方法[J].电子学报,2022,50(02):396-403.

LI Shuang-ming,GUAN Xin,WANG Hai-bin.Disjunctive Cloud Fuzzy Belief Rules for Recognition with Adaptive Parameters[J].ACTA ELECTRONICA SINICA,2022,50(02):396-403.
李双明,关欣,王海滨.参数自适应的析取云模糊置信规则识别方法[J].电子学报,2022,50(02):396-403. DOI： 10.12263/DZXB.20201253.

LI Shuang-ming,GUAN Xin,WANG Hai-bin.Disjunctive Cloud Fuzzy Belief Rules for Recognition with Adaptive Parameters[J].ACTA ELECTRONICA SINICA,2022,50(02):396-403. DOI： 10.12263/DZXB.20201253.

摘要

为获得准确的模糊置信规则结构参数，提出了参数自适应的析取云模糊置信规则识别方法.为完成模糊域的自适应划分，提出了基于频数统计的双门限检测方法和基于包含度的双门限检测方法.用云模型作为模糊集，改变熵系数和超熵系数，实现对模糊集形状的调整；前提属性的联接设置为析取逻辑关系，改进了证据的基本概率赋值方式，对规则权重和属性权重进行了优化.实验结果表明，与其他方法相比，本文方法的正确识别率提高了5%~15%，规则可解释性更强.

Abstract

In order to obtain the accurate structural parameters of fuzzy rules

a disjunctive cloud fuzzy belief rules based recognition method with adaptive parameters is proposed. In order to complete the adaptive division of fuzzy domain

a double threshold detection method based on frequency statistics and inclusion degree separately are proposed. Cloud model is used as fuzzy set

of which by changing entropy coefficient and super entropy coefficient to adjust the shape of fuzzy set

and the connection of premise attributes is set as disjunctive logic relation. The basic probability assignment of conflict evidence is improved

and a programming model is built to optimize the rule weight and attribute weight. The experimental results show that compared with other methods

the correct recognition rate of our method is improved by 5%~15%

and the rules are more interpretable.

关键词

Keywords

references

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