Name
Title | ||
|---|---|---|
Learning Concurrently Granularity, Membership Function Parameters And Rules Of Mamdani Fuzzy Rule-Based Systems |
Abstract | ||
|---|---|---|
In this paper we tackle the issue of generating Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy by using a multi-objective genetic algorithm, which concurrently learns rule base, granularity of the input and output partitions and membership function parameters. To this aim, we exploit a chromosome composed of three parts, which codify, respectively, the rule base, and, for each variable, the number of fuzzy sets and the parameters of a piecewise linear transformation of the membership functions. We show the encouraging results obtained on a real world regression problem. |
Year | Venue | Keywords |
|---|---|---|
2009 | PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | Accuracy-Interpretability Trade-off, Granularity Learning, Mamdani Fuzzy-Rule-Based Systems, Multi-objective Evolutionary Algorithms, Piecewise Linear Transformation |
Field | DocType | Citations |
Data mining,Neuro-fuzzy,Fuzzy classification,Defuzzification,Computer science,Fuzzy set,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Membership function,Fuzzy rule | Conference | 2 |
PageRank | References | Authors |
0.43 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michela Antonelli | 1 | 273 | 15.38 |
| Pietro Ducange | 2 | 566 | 27.63 |
| Beatrice Lazzerini | 3 | 715 | 45.56 |
| Francesco Marcelloni | 4 | 1404 | 91.43 |