一种基于动态聚类的模糊分类规则的生成方法

Method of Dynamic Cluster Generating Fuzzy Classifier Rules

介绍了一种基于动态聚类的模糊分类规则的生成方法,这种方法能决定规则数目,隶属函数的位置及形状.首先,介绍了基于超圆锥体隶属函数的模糊分类规则的基本形式;然后,介绍动态聚类算法,该算法能将每一类训练模式动态的分为成簇,对于每簇,则建立一个模糊规则;通过调整隶属函数的斜度,来提高对训练模式分类识别率,达到对模糊分类规则进行优化调整的目的;用两个典型的数据集评测了这篇文章研究的方法,这种方法构成的分类系统在识别率与多层神经网络分类器相当,但训练时间远少于多层神经网络分类器的训练时间.

This paper introduces a method of dynamic cluster generating fuzzy classifier rules. This method can decide the numbers of rules,position and shape of membership function. First, the fuzzy rule base with hyper cone membership is introduced. Then,the dynamic clustering arithmetic which can dynamically cluster the input training patterns is introduced. For each cluster, a fuzzy rule around a cluster center is defined. The strategy of tuning fuzzy rules is that the slopes of the membership functions are tuned successively until there is no improvement in the recognition rate of the training patterns. This method is evaluated by two data sets. The recognition rates of classifier by this method are comparable to the maximum recognition rates of the multilayered neural network classifier and the training times are much shorter.