NDLI: Entropy assessment for type-2 fuzziness

Content Provider	IEEE Xplore Digital Library
Author	Ozkan, I. Turksen, I.B.
Copyright Year	2004
Description	Author affiliation: Dept. of Mech. & Ind. Eng., Toronto Univ., Canada (Ozkan, I.)
Abstract	One of the sources of uncertainty, which perhaps is identified as parameter uncertainty, is the level of fuzziness in fuzzy system modeling. Given the optimum number of clusters and the cluster centers, one can explore type-2 membership values that capture the uncertainty of memberships. We explore variations of type-2 membership values with the entropy measure for an artificially created 12 data sets. Crisp to fuzzy data sets are constructed so that each data set has a different standard deviation within each cluster. In turn, each cluster has the same standard deviation for a given artificial data set. Members of a given artificial data set are generated randomly. Results are assessed by means of a particular entropy measure. It is shown that the content of the information uncertainty increases in certain ranges and decreases in other ranges of the level of fuzziness.
Starting Page	1111
Ending Page	1115
File Size	260152
Page Count	5
File Format	PDF
ISBN	0780383532
ISSN	10987584
DOI	10.1109/FUZZY.2004.1375566
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Fuzzy sets Uncertainty Uncertain systems Intelligent systems Industrial engineering Fuzzy systems Fuzzy logic Particle measurements Data analysis
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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