模糊数上的加权期望值算子

Qing-song Mao
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引用次数: 0

摘要

期望值算子是一种广泛应用于模糊数去模糊化的算子。它平等地对待每个$\alpha$-cut。在理论和应用中,经常需要反映$\alpha$-cuts重要性的差异。为此,我们引入了从模糊数到□的加权期望值算子,它允许对$\alpha$-cuts进行加权。证明了加权期望值算子是去模糊化算子,是可加性和尺度不变的。此外,我们根据两种类型的超空间度量:sendgraph度量和endograph度量,分别研究了加权期望值算子的连续性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weighted expected value operators on fuzzy numbers
The expected value operator is a widely used defuzzifier on fuzzy numbers. It treats each $\alpha$-cut equally. In theory and applications, it is often necessary to reflect the differences of the importance of the $\alpha$-cuts. To this aim, we introduce the weighted expected value operators from fuzzy numbers to □, which allow weighting the $\alpha$-cuts. It’s shown that the weighted expected value operators are defuzzifiers and that they are additive and scale invariant. Furthermore, we investigate the continuity of the weighted expected value operators according to two types of hyperspace metrics: the sendograph metric and the endograph metric, respectively.
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