DocumentCode :
2639693
Title :
Fuzzy tree modeling based on ε-insensitive learning method
Author :
Zhang, Wei ; Mao, Jianqin
Author_Institution :
Sch. of Autom. Sci. & Electr., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
fYear :
2011
fDate :
21-23 June 2011
Firstpage :
2074
Lastpage :
2078
Abstract :
In this paper, a new learning method tolerant to imprecision is introduced to fuzzy tree (FT) modeling method. The learning method is called ε-insensitive learning or ε learning, where, in order to fit the FT model to real data, the ε-insensitive loss function is used. FT method adaptively partitions the input space and is irrelevant to the dimension of the input space. For the consequent parameters, we use ε learning to replace the least squares estimation method which is sensitive to outliers and function influential points. Finally, numerical examples are given to demonstrate the validity of the proposed FT modeling method based on ε-insensitive learning (ε-FT).
Keywords :
fuzzy set theory; learning (artificial intelligence); trees (mathematics); ε-insensitive learning method; ε-insensitive loss function; FT modeling method; fuzzy tree modeling; Adaptation models; Binary trees; Learning systems; Mathematical model; Noise; Robustness; Training; Fuzzy Tree; insensitive learning; outliers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on
Conference_Location :
Beijing
ISSN :
pending
Print_ISBN :
978-1-4244-8754-7
Electronic_ISBN :
pending
Type :
conf
DOI :
10.1109/ICIEA.2011.5975934
Filename :
5975934
Link To Document :
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