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Article type: Research Article
Authors: Song, Jingjing | Yang, Xibei; ; | Song, Xiaoning | Yu, Hualong | Yang, Jingyu;
Affiliations: School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu Province, P.R. China | Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education, Nanjing, Jiangsu Province, P.R. China | Key Laboratory of Artificial Intelligence of Sichuan Province, Zigong, Sichuan Province, P.R. China | School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, P.R. China
Note: [] Corresponding author. Xibei Yang, School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu Province, 212003, P.R. China. Tel.: +86 0511 84433445. E-mail: [email protected]
Abstract: Hierarchy plays a crucial role in the development of the granular computing(GrC). Presently, a lot of hierarchies have been studied for characterizing the finer or coarser relationships among information granulations, which are derived from the crisp binary relations. To further improve the existing results, not only the fuzzy information granulations derived from the fuzzy binary relations are presented in this paper, but also the three orderings are explored for expressing the hierarchies on fuzzy information granulations. Furthermore, we also introduce the concepts of the global and local knowledge distances into fuzzy information granulations, which can be used to construct the knowledge distance lattices. It shows that the derived partial orderings through those lattices are corresponding to the three different hierarchies on fuzzy information granulations, respectively. The theoretical results provide us a more comprehensible perspective for the study of granular computing(GrC).
Keywords: Fuzzy information granulation, granular computing, hierarchy, knowledge distance, ordering
DOI: 10.3233/IFS-131074
Journal: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 3, pp. 1107-1117, 2014
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