文章基本信息

标题：Fuzzy Modeling using Neural Gas
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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：2019
卷号：2241
页码：409-414
出版社：Newswood and International Association of Engineers
摘要：Fuzzy modeling has been extensively studied. Among them, it has been shown that fuzzy modeling methods using vector quantization (VQ) and steepest descent method (SDM) are effective in terms of the number of rules (parameters). The methods firstly determine the initial parameter values of fuzzy rules by using VQ with learning data, and then tune the parameters by using SDM. On the other hand, Neural Gas (NG) is known as a novel approach of VQ and NG with local linear mappings (LLMs) has been applied to a time-series prediction problem. In the application, the predicted value in each of subregions is approximated by using a corresponding linear mapping. It has been demonstrated that, compared with Kmeans with RBF, NG with LLMs is advantageous in terms of the accuracy and the number of subregions. The idea of NG with LLMs has been applied to fuzzy modeling with TS fuzzy model, and its effectiveness has been demonstrated. However, the effectiveness of this approach has not been been confirmed for simpler fuzzy model such as simplified fuzzy model. This paper proposes to apply the concept of NG with LLMs to fuzzy modeling with simplified fuzzy inference model. The proposed method firstly determines the initial parameter values of fuzzy rules including the weights in the consequent part by using NG and supervised learning, and then tunes the parameters by using SDM. The effectiveness of the proposed method with simplified fuzzy modeling is demonstrated in numerical simulations of function approximation and classification problems.
关键词：Simplified Fuzzy Inference Model; Vector Quantization; Neural Gas; Local Linear Mapping