Ranking inconsistency among fuzzy, numeric, and cognitive aggregations

Lin, Hsin-Hui; Shaw, Wen-Fung

doi:10.1109/fuzzy.1998.686242

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98C

DOI: 10.1109/fuzzy.1998.686242

|View full text |Cite

Ranking inconsistency among fuzzy, numeric, and cognitive aggregations

Hsin-Hui Lin

Wen-Fung Shaw²

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Deep Properties of Totally Fuzzy Sets

Barone

2007

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

View full text Add to dashboard Cite

It was suggested in [1] that totally fuzzy sets could be transformed into "equivalent" ordinary fuzzy sets (totally fuzzy sets where all pairs of elements (x, y) are mapped to zero unless x = y) by choosing an appropriate singleton and then solving a suitable relational equation. This paper describes another method for accomplishing this transformation, namely, by taking the ordinary fuzzy set to be given by the spectrum of eigenvalues of the underlying totally fuzzy set. Special conditions are required to preserve the category-theoretic relationship between the two fuzzy sets, and these are also discussed. Once the process has been outlined, a number of possible areas of application are adumbrated. These include decision theory, linguistic hedges, and the cognitive/linguistic representation of color terms. The conclusion is that totally fuzzy sets and their spectra may have cognitive significance.

show abstract

Deep Properties of Totally Fuzzy Sets

Barone

2007

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

334 Leonard St

Brooklyn, NY 11211

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Ranking inconsistency among fuzzy, numeric, and cognitive aggregations

Cited by 1 publication

References 7 publications

Deep Properties of Totally Fuzzy Sets

Deep Properties of Totally Fuzzy Sets

Contact Info

Product

Resources

About