基于PCA的RBF神经网络预测方法研究

Research of RBF neural network prediction method based on PCA

主成分分析(PCA)法可以提取样本集的主成分,实现样本的最优压缩,从而降低样本的维数。针对用RBF神经网络进行多因素时间序列预测时样本特征指标过多的问题,提出用统计理论的PCA方法对数据进行预处理,再选出几个主成分作为神经网络的输入节点.仿真实验表明,基于PCA的RBF神经网络模型在拟合预测中与一般的RBF神经网络模型相比有较好效果,简化了网络结构,改善了预测精度.

Principal Component Analysis （PCA） method can produce optimal compression of samples and reduce dimensions of samples. In general, a RBF neural network is used to predict multi-variable time sequence, in which the number of sample＇s characteristic variables is too large to deal with. An improved method has been developed by PCA to process original data. Some principal components are selected as the input nodes of the neural network. The experiment and simulation show that the model of RBF neural network based on PCA has better effect than the common RBF neural network model, the construction of RBF network is simplified, and the precision is increased.