Improved algorithm of evidence theory based on FDR
基于FDR的证据理论改进算法

摘要

为简化证据理论合成规则融合过程，提高其融合效果，本文应用特征降维（Feature Dimension Reduction，FDB）技术，提出一种行之有效的证据理论改进算法。实验结果表明：基于FDR的证据理论改进算法具有融合过程简单、融合效果好、类型识别率高等特点，该算法经过数据集测试后，其类型识别率升高至94%，完全符合实际应用需求。希望通过这次研究，为相关人员提供有效的借鉴和参考。

Abstract

In order to simplify the fusion process of evidence theory synthesis rules and improve its fusion effect, this paper applies Feature Dimension Reduction (FDB) technology to propose an effective evidence theory improvement algorithm. The experimental results show that the improved algorithm based on FDR evidence theory has the characteristics of simple fusion process, good fusion effect, and high type recognition rate. After being tested on the dataset, the type recognition rate of the algorithm increased to 94%, fully meeting the practical application requirements. I hope to provide effective reference and guidance for relevant personnel through this study.