期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：17-22
出版社：Newswood and International Association of Engineers
摘要：It is known that learning methods of fuzzy modeling
using vector quantization (VQ) and steepest descend
method (SDM) are superior in the number of rules to other
methods using only SDM. There are many studies on how
to realize high accuracy with a few rules. Many methods
of learning all parameters using SDM are proposed after
determining initial assignments of the antecedent part of fuzzy
rules by VQ using only input information, and both input and
output information of learning data. Further, in addition to
these initial assignments, the method using initial assignment
of weight parameters of the consequent part of fuzzy rules
is also proposed. Most of them are learning methods with
simplified fuzzy inference, and little has been discussed with
TS (Takagi Sugeno) fuzzy inference model. On the other hand,
VQ method with supervised learning that divides the input
space into Voronoi diagram by VQ and approximates each
partial region with a linear function is known. It is desired
to apply the method to TS fuzzy modeling using VQ. In this
paper, we propose new learning methods of TS fuzzy inference
model using VQ. Especially, learning methods using VQ with
the supervised learning are proposed. Numerical simulations for
function approximation, classification and prediction problems
are performed to show the performance of proposed methods.
