Journal
ELECTRONICS
Volume 11, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/electronics11060941
Keywords
single-valued neutrosophic set; modified Manhattan distance; similarity measure; weighting method; multi-attribute decision making
Categories
Funding
- National Natural Science Foundation of China [71661012]
- Science Foundation for Youth Teacher of Fujian Educational Committee [JAT201026]
- Industry University Research Innovation Fund of Chinese Universities [2019ITA01053]
- Foundation of Fujian Provincial Education Department [JT180872]
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This study proposes a new distance measure and similarity measure for single-valued neutrosophic (SVN) sets based on modified Manhattan distance. The proposed measures are applied in pattern recognition, decision-making methods, and clustering algorithms.
A single-valued neutrosophic (SVN) set contains three parameters, which can well describe three aspects of an objective thing. However, most previous similarity measures of SVN sets often encounter some counter-intuitive examples. Manhattan distance is a well-known distance, which has been applied in pattern recognition, image analysis, ad-hoc wireless sensor networks, etc. In order to develop suitable distance measures, a new distance measure of SVN sets based on modified Manhattan distance is constructed, and a new distance-based similarity measure also is put forward. Then some applications of the proposed similarity measure are introduced. First, we introduce a pattern recognition algorithm. Then a multi-attribute decision-making method is proposed, in which a weighting method is developed by building an optimal model based on the proposed similarity measure. Furthermore, a clustering algorithm is also put forward. Some examples are also used to illustrate these methods.
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