一种新颖的径向基函数(RBF)网络学习算法

以提高RBF网络泛化能力为着眼点，提出了一种新型的网络结构自适应学习算法．该算法采用衰减聚类半径的聚类算法来确定初始的隐层结构，然后通过调整包含样本类别信息的扩展聚类不纯度来修正隐层结构，直至满足所有扩展聚类不纯度均小于等于不纯度均值以及所有扩展聚类方差均不超过方差均值这两个条件．这样就确定了隐层的最终结构．在确定隐层结构之后，采用反向传播算法来训练隐层与输出层之间的连接权重．经双螺旋线问题仿真试验验证，该算法确实具有较强的泛化能力．

An Original RBF Network Learning Algorithm

This paper proposes an original RBF network structure learning algorithm aiming at improving its generalization ability. The algorithm determines the initial network hidden structure by decayed radius clustering algorithm, then modifies the hidden structure by adjusting the impurity of generalized clusters containing the classification information of the training samples until the conditions that all clusters' impurities are less than the average impurity level and all variants are less than the average variant level are satisfied. Then we get the final hidden structure. After determining the hidden structure, the back-propagation algorithm is used to training the weights between the hidden layer and output layer. The experiment of two spirals problem proves that our algorithm has higher generalization ability indeed.