基于三阶段RBFNN学习算法的复杂样本分类研究

以提高径向基函数神经网络(radial basis function neural network,RBFNN)的分类能力为出发点,把衰减半径聚类的思想与误差平方和准则结合起来,提出了RBFNN三阶段学习算法。该算法先利用动态衰减半径聚类确定隐节点的初始结构,再由误差平方和准则进行中心点微调,并用类内类间距确定径基宽度,最后采用伪逆法训练隐层与输出层间的连接权重。给出了算法的具体步骤,并通过Iris和WINES数据集的仿真实验,证明该算法确实具有较强的分类能力。

Three-phase RBFNN learning algorithm for complex classification

To improve radial basis function neural network(RBFNN) classification ability,a three-phase RBFNN learning algorithm is proposed.Firstly,the initial hidden structure of the network is determined by dynamic decayed radius clustering algorithm.Then the hidden centers are modified by the sum squared error(SSE) rule,and the radius widths are calculated with the within-cluster and between-cluster distances.Finally the pseudo-inverse algorithm is utilized to train the weights between the hidden layer and the output layer.The experiments are implemented on Iris and Wines datasets,which shows that the proposed RBFNN training algorithm has a higher classification ability compared with the conventional methods.