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A new method to rank fuzzy numbers using Dempster-Shafer theory with fuzzy targets

机译:基于带模糊目标的Dempster-Shafer理论对模糊数进行排序的新方法

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In this paper, an extended ranking method for fuzzy numbers, which is a synthesis of fuzzy targets and the Dempster-Shafer Theory (DST) of evidence, is devised. The use of fuzzy targets to reflect human viewpoints in fuzzy ranking is not new. However, different fuzzy targets can lead to contradictory fuzzy ranking results; making it difficult to reach a final decision. In this paper, the results from different viewpoints are treated as different sources of evidence, and Murphy's combination rule is used to aggregate the fuzzy ranking results. DST allows fuzzy numbers to be compared and ranked while preserving their uncertain and imprecise characteristics. In addition, a hybrid method consisting of fuzzy targets and DST with the Transferable Belief Model is formulated, which fulfils a number of important ordering properties. A series of empirical experiments with benchmark examples has been conducted and the experimental results clearly indicate the usefulness of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.
机译:本文设计了一种模糊数的扩展排序方法,该方法是模糊目标和证据的Dempster-Shafer理论(DST)的综合。在模糊等级中使用模糊目标反映人类观点并不是什么新鲜事。但是,不同的模糊目标可能导致相互矛盾的模糊排名结果。因此很难做出最终决定。在本文中,将来自不同观点的结果视为不同的证据来源,并使用墨菲的组合规则来汇总模糊排名结果。 DST允许对模糊数字进行比较和排序,同时保留其不确定性和不精确性。此外,还提出了一种由模糊目标和DST组成的混合方法以及可转移的信念模型,该方法满足了许多重要的排序特性。进行了一系列带有基准示例的实验,实验结果清楚地表明了该方法的有效性。 (C)2016 Elsevier Inc.保留所有权利。

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