0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessThe symmetric implicational method is revealed from a different perspective based upon the restriction theory, which results in a novel fuzzy inference scheme called the symmetric implicational restriction method.Initially, the SIR-principles are put forward, which constitute optimized versions of the triple I restriction inference mechanism.Next, the existential requirements of basic solutions are given.The supremum (or infimum) of its basic solutions is achieved from some properties of fuzzy implications.The conditions are obtained for the supremum to become the maximum (or the infimum to be the minimum).Lastly, four concrete examples are provided, and it is shown that the new method is better than the triple I restriction method, because the former is able to let the inference more compact, and lead to more and superior particular inference schemes.
Yiming Tang, Wenbin Wu, Youcheng Zhang, Witold Pedrycz, Fuji Ren, Jun Liu (2021). Symmetric implicational restriction method of fuzzy inference. , DOI: https://doi.org/10.14736/kyb-2021-4-0688.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2021
Authors
6
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.14736/kyb-2021-4-0688
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access