Abstract
It is known that removal of those redundant fuzzy rules from a rule base can result in a more compact fuzzy model with better generalizing ability. In this paper we propose a number of orthogonal transformation based methods which provide new or alternative tools for rule extraction. A common attribute of these methods is that they all work on a truth value matrix and employ some measure index to detect the rules that should be retained and eliminated. The performance of these methods is illustrated using a nonlinear plant modeling example.
| Original language | English (US) |
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| Title of host publication | IEEE International Conference on Fuzzy Systems |
| Publisher | IEEE |
| Pages | 253-258 |
| Number of pages | 6 |
| Volume | 1 |
| State | Published - 1997 |
| Event | Proceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) - Barcelona, Spain Duration: Jul 1 1997 → Jul 5 1997 |
Other
| Other | Proceedings of the 1997 6th IEEE International Conference on Fussy Systems, FUZZ-IEEE'97. Part 1 (of 3) |
|---|---|
| City | Barcelona, Spain |
| Period | 7/1/97 → 7/5/97 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Software
- Artificial Intelligence
- Applied Mathematics