Participatory Evolving Fuzzy Modeling

Author

Lima, Elton ; Gomide, Fernando ; Ballini, Rosangela

Author_Institution

Fac. of Electr. & Comput. Eng., State Univ. of Campinas

fYear

2006

fDate

7-9 Sept. 2006

Firstpage

36

Lastpage

41

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

Keywords

adaptive systems; fuzzy set theory; fuzzy systems; knowledge based systems; learning (artificial intelligence); adaptive fuzzy system; fuzzy modeling; fuzzy rule-based model; participatory learning; unsupervised dynamic fuzzy clustering; Adaptive systems; Clustering algorithms; Data mining; Fuzzy sets; Fuzzy systems; Genetic algorithms; Heuristic algorithms; Hydroelectric power generation; Page description languages; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolving Fuzzy Systems, 2006 International Symposium on

Conference_Location

Ambleside

Print_ISBN

0-7803-9718-5

Electronic_ISBN

0-7803-9719-3

Type

conf

DOI

10.1109/ISEFS.2006.251135

Filename

4016699