Novel a-divergence measures on picture fuzzy sets and interval-valued picture fuzzy sets with diverse applications
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Currently, many studies have developed distance or divergence measures between intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IvFSs). As a generalization of IFSs, picture fuzzy sets (PFSs) provide a more nuanced representation of uncertain and ambiguous information. Interval-valued picture fuzzy sets (IvPFSs) combine the concepts of IvIFSs and PFSs, providing a highly effective means of representing and processing uncertain, ambiguous and incomplete information. How to better measure the differences between PFSs and IvPFSs is still an open issue. This paper proposes some novel a-divergence measures for PFSs and IvPFSs, respectively. We demonstrate the basic properties of the proposed divergence measures, including non- negativity, non-degeneracy and symmetry. Besides, we analyze some special cases of the proposed divergence measures that degenerate into or are related to several well-known divergences. Then, we construct some numerical examples to demonstrate the effectiveness of the proposed measures concerning existing measures. Finally, the proposed a-divergence measures are applied to pattern recognition, multi-attribute decision-making (MADM) and clustering, demonstrating that these measures possess a high confidence level and can produce trustworthy results, especially in comparable situations.
Açıklama
Anahtar Kelimeler
Picture Fuzzy Sets, Interval-Valued Picture Fuzzy Sets, Alpha-Divergence, Pattern Recognition, Multi-Attribute Decision-Making, Clustering
Kaynak
Engineering Applications of Artificial Intelligence
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
136
