DocumentCode :
468366
Title :
A New Fuzzy Modeling Approach Based on Support Vector Regression
Author :
Yu, Long ; Xiao, Jian ; Bai, Yifeng
Author_Institution :
Southwest Jiaotong Univ., Chengdu
Volume :
3
fYear :
2007
fDate :
24-27 Aug. 2007
Firstpage :
578
Lastpage :
584
Abstract :
New interpretable kernels created by conjoining the univariate fuzzy membership functions with a t-norm operator are proposed in this paper. Based on support vector regression with presented kernel, a learning algorithm consisting of two phases is developed to construct fuzzy system. In the first phase, the support vector regression learning model provides architecture to extract support vectors for generating fuzzy rules, and then characterizes the support vector expansion in TS fuzzy inference procedure through simple equivalent transform. In the second phase, a reduced set method is employed to simplify the obtained fuzzy model, and a bottom-up strategy with relative degree of sharing is suggested to obtain a transparent rule base, at the same time preserves the accuracy and generalization performance of the fuzzy model. Finally, the performance of the proposed fuzzy model is compared with hierarchical clustering based on using a self-organizing network modeling methods.
Keywords :
fuzzy set theory; inference mechanisms; learning (artificial intelligence); regression analysis; support vector machines; bottom-up strategy; fuzzy inference; fuzzy modeling approach; hierarchical clustering; learning algorithm; self-organizing network modeling methods; set method; support vector regression; univariate fuzzy membership functions; vector expansion; Character generation; Fuzzy sets; Fuzzy systems; Inference algorithms; Kernel; Learning systems; Machine learning; Risk management; Self-organizing networks; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
Type :
conf
DOI :
10.1109/FSKD.2007.78
Filename :
4406304
Link To Document :
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