Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/2912

 Title: A Method to Construct an Extension of Fuzzy Information Granularity Based on Fuzzy Distance Authors: Thien, Nguyen VanDemetrovics, JanosThi, Vu DucGiang, Nguyen LongSon, Nguyen Nhu Keywords: Granular ComputingFuzzy Granular StructureFuzzy Information GranuleFuzzy Information GranularityFuzzy Distance Issue Date: 2016 Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences Citation: Serdica Journal of Computing, Vol. 10, No 1, (2016), 013p-030p Abstract: In fuzzy granular computing, a fuzzy granular structure is the collection of fuzzy information granules and fuzzy information granularity is used to measure the granulation degree of a fuzzy granular structure. In general, the fuzzy information granularity characterizes discernibility ability among fuzzy information granules in a fuzzy granular structure. In recent years, researchers have proposed some concepts of fuzzy information granularity based on partial order relations. However, the existing forms of fuzzy information granularity have some limitations when evaluating the fineness/coarseness between two fuzzy granular structures. In this paper, we propose an extension of fuzzy information granularity based on a fuzzy distance measure. We prove theoretically and experimentally that the proposed fuzzy information granularity is the best one to distinguish fuzzy granular structures. ACM Computing Classification System (1998): I.5.2, I.2.6. URI: http://hdl.handle.net/10525/2912 ISSN: 1312-6555 Appears in Collections: Volume 10 Number 1

Files in This Item:

File Description SizeFormat
sjc-vol10-num1-2016-p013-p030.pdf216.6 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.