An improved α-cut approach to transforming fuzzy membership function into basic belief assignment

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• 作者：Yi Yang^aAuthor Vitae ; X. Rong Li^bAuthor Vitae ; Deqiang Han^c ; ^{deqhan@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Belief functions ; Evidence theory ; Fuzzy sets ; Membership functions ; Uncertainty
• 刊名：Chinese Journal of Aeronautics
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：29
• 期：4
• 页码：1042-1051
• 全文大小：1273 K

文摘

In practical applications, pieces of evidence originated from different sources might be modeled by different uncertainty theories. To implement the evidence combination under the Dempster–Shafer evidence theory (DST) framework, transformations from the other type of uncertainty representation into the basic belief assignment are needed. α-Cut is an important approach to transforming a fuzzy membership function into a basic belief assignment, which provides a bridge between the fuzzy set theory and the DST. Some drawbacks of the traditional α-cut approach caused by its normalization step are pointed out in this paper. An improved α-cut approach is proposed, which can counteract the drawbacks of the traditional α-cut approach and has good properties. Illustrative examples, experiments and related analyses are provided to show the rationality of the improved α-cut approach.