一种基于径向基神经网络的组合预测模型-《郑州大学学报(工学版)》

文章信息/Info

Title:: A combinatorial predictive model based on a radial basis neural network

Keywords:: combinatorial predictive models; nearest neighbor clustering algorithm; Radial basis; RBF neural networks; Traffic flow

摘要:: 根据基于最近邻聚类算法(NNCA)的径向基(RBF)神经网络和自回归求和滑动平均(ARIMA)两种方法,建立了各自的单项预测子模型,并利用RBF神经网络对两个单项预测子模型结果进行组合预测,得到最终的预测值.将该模型应用于长沙市某路段的交通流量预测,实验结果证明了该预测模型的有效性,采用组合预测模型比单一预测模型的预测精度有了较大提高.

Abstract:: According to the Radial Basis (RBF) neural network and autoregressive sum sliding average (ARIMA) based on nearest neighbor clustering algorithm (NNCA), their respective single-term prediction submodels are established, and the RBF neural network is used to combine the results of the two single-term prediction submodels to obtain the final prediction value. The experimental results prove the effectiveness of the model by applying the model to the traffic flow prediction of a certain road section in Changsha, and the prediction accuracy of the combined prediction model is greatly improved compared with the single prediction model.