An Improved Deng Entropy and Its Application in Pattern Recognition

被引：100

作者：

Cui, Huizi ^{[1
]}

Liu, Qing ^{[1
]}

Zhang, Jianfeng ^{[1
]}

Kang, Bingyi ^{[1
]}

机构：

[1] Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Shaanxi, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

关键词：

Entropy; Deng entropy; Shannnon entropy; Dempster-Shafer evidence theory; pattern recognition; COMPLEX ELECTROMECHANICAL SYSTEMS; RELIABILITY-ANALYSIS; UNCERTAINTY MEASURE; MATHEMATICAL-THEORY; FUSION; FRAMEWORK; EVIDENCES; MODEL; SPECIFICITY; CONFLICT;

D O I：

10.1109/ACCESS.2019.2896286

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

How to manage the uncertainty of the basic probability assignment accurately and efficiently is of significance and also an open issue. Plenty of functions have been established to cover the issue, especially Deng entropy recently. Deng entropy can deal with the more complex situation of the focal elements (propositions). However, Deng entropy has some limitations when the propositions are of the intersection. In this paper, a modified function is proposed by considering the scale of the frame of discernment and the influence of the intersection between statements on uncertainty. The proposed belief entropy provides a promising way to measure the uncertain information. Some numerical examples and an application in pattern recognition are used to show the efficiency and accuracy of the proposed belief entropy.

引用

页码：18284 / 18292

页数：9

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