Name
Abstract | ||
|---|---|---|
The synthesis of fuzzy systems involves the identification of a structure and its specialization by means of parameter optimization. In doing this, symbolic approaches which encode the structure information in the form of high-level rules allow further manipulation of the system to minimize its complexity, and possibly its implementation cost, while all-parametric methodologies often achieve better approximation performance. In this paper, we rely on the concept of a fuzzy set of rules to tackle the rule induction problem at an intermediate level. An online adaptive algorithm is developed which almost surely learns the extent to which inclusion of a rule in the rule set significantly contributes to the reproduction of the target behavior. Then, the resulting fuzzy set of rules can be defuzzified to give a conventional rule set with similar behavior. Comparisons with high-level and low-level methodologies show that this approach retains the most positive features of both |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/91.493903 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
target behavior,structure information,high-level rule,fuzzy set,conventional rule,fuzzy system,all-parametric methodology,similar behavior,system identification,approximation performance,rule induction problem,fuzzy set theory,fuzzy sets,context modeling,fuzzy systems,upper bound,data mining,adaptive systems,shape,identification,knowledge based systems | Fuzzy classification,Defuzzification,Control theory,Fuzzy set operations,Fuzzy set,Rule induction,Artificial intelligence,Fuzzy control system,Type-2 fuzzy sets and systems,Fuzzy number,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 2 | 1063-6706 |
Citations | PageRank | References |
30 | 2.43 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Rovatti | 1 | 402 | 44.72 |
| R Guerrieri | 2 | 124 | 30.04 |