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Analysing fuzzy sets through combining measures of similarity and distance

McCulloch, Josie; Wagner, Christian; Aickelin, Uwe

Authors

Josie McCulloch

Uwe Aickelin



Abstract

Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are generated from real data, and while two different measures may be used to automatically compare such fuzzy sets, it is difficult to interpret two different results. This is especially true where a large number of fuzzy sets are being compared as part of a reasoning system. This paper introduces a method for combining the results of multiple measures into a single measure for the purpose of analysing and comparing fuzzy sets. The combined measure alleviates ambiguous results and aids in the automatic comparison of fuzzy sets. The properties of the combined measure are given, and demonstrations are presented with discussions on the advantages over using a single measure.

Publication Date Sep 8, 2014
Peer Reviewed Peer Reviewed
APA6 Citation McCulloch, J., Wagner, C., & Aickelin, U. (2014). Analysing fuzzy sets through combining measures of similarity and distance
Keywords Fuzzy, Logic
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6891672
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, NJ : IEEE,[2014. (ISBN: 9781479920730), pp. 155-162 (doi: 10.1109/FUZZ-IEEE.2014.6891672). © 2014 IEEE

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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