文章基本信息

标题：Fuzzy Modeling using Vector Quantization with Supervised Learning
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作者：Hirofumi Miyajima ; Noritaka Shigei ; Hiromi Miyajima 等
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.
关键词：Fuzzy Inference Systems; Vector Quantization; Neural Gas; Vector Quantization with Supervised Learning; Appearance Frequency;