基于非重叠输入划分的模糊分类规则生成

L. Mikhailov
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引用次数: 3

摘要

提出了一种从数值数据中生成模糊分类规则的新方法。该方法的主要思想是将输入特征空间划分为多个不重叠的超框,每个超框只包含一个分类类的输入数据,然后为每个超框生成模糊规则和隶属函数。提出了一种适当的模糊推理机制,用于将新的输入数据分类到输出分类空间中。该方法使基于模糊规则的系统的综合形式化,也可用于模糊控制系统的函数逼近和设计。利用Fisher虹膜数据,将该方法与现有的模糊分类方法进行了数值比较。对比结果表明,该方法优于大多数方法,可以成功地用于模糊分类器的开发
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generation of Fuzzy Classification Rules by Non-Overlapping Input Partitioning
The paper proposes a new method for generating fuzzy classification rules from numerical data. The main idea of the method consists in separating the input feature space into a number of non-overlapping hyperboxes, which contain input data from one classification class only, and a consequent generation of fuzzy rules and membership functions for each hyperbox. An appropriate fuzzy inference mechanism is proposed for classifying new input data into the output classification space. The proposed method formalizes the synthesis of fuzzy rule-based systems and could also be used for function approximation and design of fuzzy control systems. The method is numerically compared to some existing fuzzy classification methods using the Fisher iris data. The comparison results show that it outperforms most of them and can successfully be used for the development of fuzzy classifiers
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