规则数确定的自适应模糊分类器

doi:10.3969/j.issn.1001-2400.2017.02.015

摘要/Abstract

摘要：

为使模糊分类器能够利用自适应模糊系统的诸多优点，并避免随着输入空间维度增加出现“维度灾难”，提出一种新的模糊规则数确定的自适应模糊分类器．新分类器由多个用于区分出一种类别的模糊推理机组成，每个模糊推理机包含两条模糊逻辑“如果-则”规则，分类器的总规则数由待分类模式的类别数确定，分类器采用误差反向传播学习算法进行学习训练．对比反向传播神经网络分类器，通过对鸢尾花卉数据集与Ripley合成数据集的分类测试，表明新分类器具有较强的学习能力、分类识别能力，以及抗干扰、抗数据污染能力．

关键词: 模糊逻辑, 模糊推理, 模糊规则, 分类, 适应模糊分类器

Abstract:

The adaptive fuzzy system has many advantages over the neural network. It can be used to design a fuzzy classifier. However, there is the “Dimension Calamity” with the number of inputs increasing in the adaptive fuzzy system. In this paper, an adaptive fuzzy classifier with a fixed number of fuzzy rules is proposed. This classifier is combined by several fuzzy reasoning machines so that one fuzzy reasoning machine recognizes only one class. Every fuzzy reasoning machine includes two “If-Then” fuzzy rules. The total fuzzy rules number of the classifier is confirmed by the number of classes of the patterns being classified. The classifier uses the “Error Back-Propagation Training” arithmetic as the learning arithmetic. Compared with the BP neural network classifier, the new classifier and BP neural network classifier are both tested by the famous iris dataset and Ripley's synthetic dataset. It is proved that the new classifier has a good classification ability and learning ability even if the data have been polluted.

Key words: fuzzy logic, fuzzy reasoning, fuzzy rule, classification, adaptive fuzzy classifier

石绍应;王小谟;曹晨;张靖. 规则数确定的自适应模糊分类器[J]. 西安电子科技大学学报, 2017, 44(2): 81-87.

SHI Shaoying;WANG Xiaomo;CAO Chen;ZHANG Jing. Adaptive fuzzy classifier with a fixed number of fuzzy rules[J]. Journal of Xidian University, 2017, 44(2): 81-87.

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