Fuzzy Roughness Measurement Model Based on Membership Effect
- DOI
- 10.2991/icwcsn-16.2017.87How to use a DOI?
- Keywords
- Fuzzy set; Roughness; Fuzzy rough set; Membership effect function; Attribute reduction
- Abstract
Rough fuzzy set theory and fuzzy set theory are two commonly used tools in today's uncertain type of information processing. How to construct a fusion method for two kinds of uncertain information systematically has been a focus in both academic and applied fields. By analyzing the characteristics and shortcomings of the current fuzzy roughness sets, this paper puts forward concept of membership effect function and establishes a fuzzy roughness measurement model based on membership effect (denoted by FRM-BME, for short). And then, several necessary and sufficient conditions are given to reflect the value of FRM-BME. Finally, we propose an attribute reduction algorithm based on FRM-BME, and further analyze the characteristics and effectiveness of FRM-BME combined with specific cases. Theoretical analysis and experimental results show that, FRM-BME not only has good structural characteristics and interpretability, but also can simply integrate the fuzzy processing preference into roughness measurement system. To a certain extent, it not only enriches the existing related theories, but also can be widely used in artificial intelligence, resource management and many other fields.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Fa-Chao Li AU - Li-Kun Wang PY - 2016/12 DA - 2016/12 TI - Fuzzy Roughness Measurement Model Based on Membership Effect BT - Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016) PB - Atlantis Press SP - 411 EP - 416 SN - 2352-538X UR - https://doi.org/10.2991/icwcsn-16.2017.87 DO - 10.2991/icwcsn-16.2017.87 ID - Li2016/12 ER -