| 郭延芬,李泰.基于模糊K-均值算法的模糊分类器设计[J].声学技术,2007,(4):701~703 |
| 基于模糊K-均值算法的模糊分类器设计 |
| Design of fuzzy K-means-based fuzzy classifier |
| 投稿时间：2006-04-14 修订日期：2006-08-06 |
| DOI： |
| 中文关键词: 模式识别 模糊分类器 模糊K-均值算法 |
| 英文关键词: pattern recognition fuzzy classifier fuzzy k-means algorithm |
| 基金项目: |
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| 摘要点击次数: 1057 |
| 全文下载次数: 1071 |
| 中文摘要: |
| 基于模糊K-均值算法的模糊分类器,就是把目前比较常用的模糊K-均值算法的聚类方法,再一次与模糊分类规则提取相结合而得到的一种分类器。它是一种很有效的模糊分类器,训练样本能正确的分类。在这种方法中,首先用模糊K-均值算法按剖分和覆盖的原则把训练样本分成群,并且每一群的中心和半径都被计算出来。然后,设计一个用模糊规则来表示分类的模糊系统。这样就有效地构建了一个能对训练样本比较准确分类的模糊分类器。用这种方法设计的分类器不需要预定义参数、训练时间较短、方法简单 |
| 英文摘要: |
| The fuzzy k-means-based fuzzy classifier combines clustering of fuzzy k-means algorithm with a fuzzy rule tractor.We propose to efficiently design a fuzzy classifier so that the training patterns can be correctly classified.This method follows the principle of partitioning and covering technique.The fuzzy k-means algorithm is first used to partition the training data for each class into several clusters,and the cluster center and the radius for each cluster are calculated.A fuzzy system design method that uses a fuzzy rule to represent a cluster is then proposed so that a fuzzy classifier can be efficiently constructed to correctly classify the training data.The proposed method does not need a prior parameter definition,but only needs a short training time,therefore is simple. |
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