一种基于聚类的RBF神经网络模型

A Model of RBF Neuron Network Based on Clustering

应用于工程中的径向基函数(RBF)神经网络模型,当训练样本量很大时常存在训练学习速度慢和拟合精度不高的问题.针对这一情况提出了一种基于聚类的RBF神经网络(BC-RBFNN)模型.该模型首先通过对训练样本进行聚类分析,将其分为不同的类,接着根据每类的训练样本构造一个RBF子网并获取其相关参数,接着再将各个子网组织成一个BC-RBFNN模型.通过对其进行理论分析和性能检验试验,结果表明该模型能加快网络训练学习速度,缩小的模型规模,提高网络的预测精度.

In the paper, a model of RBF Neuron Network called BC - RBFNN was presented based on clustering. The model was made up of many RBF subsidiary networks, and every RBF subsidiary network formed according to the clustering of training data. Investigations reveal that the model possesses desired property. It quickens the learning process, reduces the size and improves the forecasting precision of network model. In the end, theoretical analysis and experiments were performed on the model. The results show that the BC - RBFNN model achieves faster speed, smaller size and higher predicting theoretical that of than the static RBFNN model.