DocumentCode :
2301228
Title :
Evolving fuzzy linear regression trees
Author :
Lemos, Andre ; Caminhas, Walmir ; Gomide, Fernando
Author_Institution :
Dept. of Electr. Eng., Fed. Univ. of Minas Gerais, Belo Horizonte, Brazil
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
Abstract :
This paper introduces a new approach for evolving fuzzy modeling based on a tree structure. The system is a fuzzy linear regression tree whose topology can be continuously updated using a statistical model selection test. A fuzzy linear regression tree is a fuzzy tree with linear model in each leaf. The evolving linear regression approach is evaluated on a forecasting problem and its performance compared against alternative evolving fuzzy models and classic models with fixed structures. The results suggest that evolving fuzzy regression tree is a promising approach for adaptive system modeling.
Keywords :
forecasting theory; fuzzy set theory; regression analysis; statistical testing; topology; trees (mathematics); adaptive system modeling; evolving fuzzy modeling; evolving fuzzy regression tree; forecasting problem; fuzzy linear regression trees; linear model; statistical model selection test; topology; tree structure; Adaptation model; Computational modeling; Data models; Fuzzy sets; Input variables; Linear regression; Regression tree analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
ISSN :
1098-7584
Print_ISBN :
978-1-4244-6919-2
Type :
conf
DOI :
10.1109/FUZZY.2010.5583970
Filename :
5583970
Link To Document :
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