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Semantic Risk Analysis Based on Single-Valued Neutrosophic Sets

Fuzzy risk analysis is diffusely applied in risk assessment of components by the semantic model. Due to the fuzzy characteristic in the process of fuzzy risk analysis, analysis parameters are imprecise and vague. Therefore, the determination of the risk of failure is challenging part of fuzzy risk analysis with existing methods. Hence, in this paper, a semantic risk analysis method based on the technique for order performance by similarity to ideal solution (TOPSIS) under a single-valued neutrosophic set (SVNS) is presented. First, a five-member linguistic term set is introduced and these linguistic terms are expressed in terms of the generalized trapezoidal fuzzy numbers. Then, the linguistic term is transformed into the SVNS and generated SVNS is fused by single-valued neutrosophic prioritized weighted average (SVNPWA) operator. On this basis, the TOPSIS approach is used to obtain the final rank to ascertain future risk. Finally, a fuzzy risk analysis example is conducted to illustrate the effectiveness of the proposed method. Further, the out-performance of the proposed method is illustrated in comparisons to the existing methods.