Rough fuzzy hybridization
From Wikipedia, the free encyclopedia
Rough fuzzy hybridization is a method of hybrid intelligent system or soft computing, where Fuzzy set theory is used for linguistic representation of patterns, leading to a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space.
ClassHybrid intelligent system
Data structureGranulated feature space
Worst-case performanceVaries by implementation
Worst-case space complexityVaries by implementation
| Rough fuzzy hybridization | |
|---|---|
| Class | Hybrid intelligent system |
| Data structure | Granulated feature space |
| Worst-case performance | Varies by implementation |
| Worst-case space complexity | Varies by implementation |
