Name
Abstract | ||
|---|---|---|
A general framework to construct fuzzy rule systems automatically from given examples is proposed in this paper. The objective is to generate fuzzy systems with good mapping ability and generalization ability as well. The procedure consists of five steps. Cluster analysis of data is used for initial fuzzy region partitions. Resolution to rule conflict is included in the fuzzy defuzzification method. Inductive learning algorithm is incorporated to enhance generalization ability of fuzzy systems. System performance is iteratively improved by further partitioning fuzzy regions. Ineffective attributes can be implicated by the tree structure resulting from the learning algorithm and eliminated without deteriorating system performance. Several experiments are conducted to show advantages of this framework. |
Year | Venue | Keywords |
|---|---|---|
1999 | Integrated Computer-Aided Engineering | generalization ability,system performance,fuzzy system,initial fuzzy region partition,general framework,fuzzy rule system,partitioning fuzzy region,fuzzy defuzzification method,cluster analysis,good mapping ability |
Field | DocType | Volume |
Data mining,Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Mathematics,Fuzzy rule | Journal | 6 |
Issue | ISSN | Citations |
3 | 1069-2509 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Te-Min Chang | 1 | 34 | 6.29 |
| Yuehwern Yih | 2 | 219 | 18.32 |