摘要
提出一种从训练样本提取基于超盒表示的模糊规则的方法,用于模式分类.这种方法把模式空间划分成模糊超盒,作为模糊规则的前件,规则的后件是相应的类别名称,同时给出每一条模糊规则的置信度.模糊分类规则从训练样本通过学习算法提取.规则提取方法可以分为,对于单个训练模式进行规则前件和后件的局部在线学习,和对于全部训练模式进行循环学习.实验显示规则提取的过程,说明通过这种方法能够获得有效的模式分类规则.
In this paper,we discuss a new method for rule extraction based on hyper-box representation.The method is deducted from the MRCC model,used for pattern classification.The method partitions the pattern space with multi-dimensional fuzzy hyper-boxes , and assigns a class label with certainty degree for each variable fuzzy region. These rules are extracted from numerical data through a recursive learning procedure. Experiment shows the efficiency of rule extraction procedure.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2002年第9期1379-1383,共5页
Acta Electronica Sinica
基金
高等学校博士学科点专项科研基金资助课题(No.98000338)
