一种快速高效的神经网络分类器

提出了一种新型的基于竞争型神经网络的学习算法，该算法综合了竞争型神经网络和层次聚类的特点，通过竞争型神经两络对对象进行初步分类，并在隐含层采用Hcbb学习规则对子类进行关联学习，学习速度快，分类质量好，可以对任意形状、任意大小的簇进行聚类，同时不受噪音的影响，是一种快速高效的分类算法。

A Fast and High Efficient Neural Network Classifier

This paper presents a new neural classification algorithm based on the competitive neural network. Combining the competitive neural network and the hierarchical clustering, the new model classifies the objects first, then uses Hebb learning rule to connect the sub clusters which are activated, finally merges the same connected sub graph of the same output nerve cells after deleting the infirm links between the nerve cells in the recessive layer. Out of the noise influence, this neural network model can classify the clusters which have the random form or random size. With last learning and good result of the classification, the model is a good classifier of the multidimensional data.