O. Kaynak et al. (Eds.): ICANN/ICONIP 2003, LNCS 2714, pp. 225–233, 2003.
© Springer-Verlag Berlin Heidelberg 2003
Fuzzy Model Identification Using Support Vector
Clustering Method
$\úHJOUçar
1
, Yakup Demir
1
, and Cüneyt *]HOLú
2
1
Electrical and Electronics Engineering Department,
Engineering Faculty, )ÕUDW University, Elazig, Turkey
agulucar@ieee.org,ydemir@firat.edu.tr
2
Electrical and Electronics Engineering Department,
Dokuz Eylül University, Kaynaklar Campus, ø]PLU7XUNH\
guzelis@eee.deu.edu.tr
Abstract. We have observed that the support vector clustering method proposed
by Asa Ben Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik, (Journal
of Machine Learning Research, (2001), 125–137) can provide cluster bounda-
ries of arbitrary shape based on a Gaussian kernel abstaining from explicit cal-
culations in the high-dimensional feature space. This allows us to apply the
method to the training set for building a fuzzy model. In this paper, we sug-
gested a novel method for fuzzy model identification. The premise parameters
of rules of the model are identified by the support vector clustering method
while the consequent ones are tuned by the least squares method. Our model
does not employ any additional method for parameter optimization after the ini-
tial model parameters are generated. It gives also promising performances in
terms of a large number of rules. We compared the effectiveness and efficiency
of our model to the fuzzy neural networks generated by various input space-
partition techniques and some other networks.
1 Introduction
In recent years, fuzzy models have successfully appeared on a lot of applications in
system identification, control, prediction and inference. An important property of
fuzzy models is their ability to represent highly nonlinear systems. In comparison with
other nonlinear black-box modeling techniques, fuzzy models have the advantage of
giving insight into the relations between model variables and combining prior knowl-
edge with the information identified from numerical data.
The fuzzy models are accomplished by structure identification and parameter ad-
justment procedures. Generally, these models are built from two learning phases, the
structure learning phase and the parameter learning phase. These two phases are usu-
ally done sequentially; the structure learning phase is employed to decide the structure
of fuzzy rules first and then the parameter learning phase is used to fine tune the coef-
ficients of each rule obtained from the first one. Various methods have been proposed
to solve these problems separately or in a combinatorial way [1–3].