基于模糊K-均值算法的模糊分类器设计

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

Design of fuzzy K-means-based fuzzy classifier

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 c-luster 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.