| Full text | |
| Author(s): |
Lima, Elton
;
Gomide, Fernando
;
Ballini, Rosangela
;
Angelov, P
;
Filev, D
;
Kasabov, N
;
Cordon, O
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2006 INTERNATIONAL SYMPOSIUM ON EVOLVING FUZZY SYSTEMS, PROCEEDINGS; v. N/A, p. 2-pg., 2006-01-01. |
| Abstract | |
This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving participatory learning is used to forecast average weekly inflows for hydroelectric generation purposes and compared with eTS, an evolving modeling technique that uses the notion of potential to dynamically cluster data.(12) (AU) | |
| FAPESP's process: | 03/10019-9 - Forecasting methodologies for energy systems |
| Grantee: | Fernando Antonio Campos Gomide |
| Support Opportunities: | Research Projects - Thematic Grants |