New Similarity Measures of Simplified Neutrosophic Sets and Their Applications
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The simplified neutrosophic set (SNS) is a generalization of fuzzy set that is designed for some practicalsituations in which each element has truth membership function, indeterminacy membership function andfalsity membership function. In this paper, we propose a new method to construct similarity measures ofsingle valued neutrosophic sets (SVNSs) and interval valued neutrosophic sets (IVNSs), respectively. Then weprove that the proposed formulas satisfy the axiomatic definition of the similarity measure. At last, we applythem to pattern recognition under the single valued neutrosophic environment and multi-criteria decisionmakingproblems under the interval valued neutrosophic environment. The results show that our methodsare effective and reasonable.
Abstract
The simplified neutrosophic set (SNS) is a generalization of fuzzy set that is designed for some practicalsituations in which each element has truth membership function, indeterminacy membership function andfalsity membership function. In this paper, we propose a new method to construct similarity measures ofsingle valued neutrosophic sets (SVNSs) and interval valued neutrosophic sets (IVNSs), respectively. Then weprove that the proposed formulas satisfy the axiomatic definition of the similarity measure. At last, we applythem to pattern recognition under the single valued neutrosophic environment and multi-criteria decisionmakingproblems under the interval valued neutrosophic environment. The results show that our methodsare effective and reasonable.
참고문헌 (24)
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