基于径向基神经网络的大连站客运量预测

针对铁路客运量在时序上的复杂非线性特征,采用径向基函数(RBF)神经网络对铁路客运量时间序列进行预测.用自相关分析技术分析时间序列的延迟特性,据此确定RBF神经网络的输入、输出向量,建立了基于MATLAB7.0环境下的RBF神经网络客运量预测模型,并用大连站实际客运量数据进行了验证.结果表明,该模型拟合精度和预测精度较高、计算速度较快.

Prediction of Dalian Station Passenger Volume Based on RBF Neural Network

Radial basis function(RBF) neural network is adopted to predict the time serial of the railway passenger volume and the delayed character of time serial is analyzed by the technique of self-relativity analysis.Based on the analyzed result,the input and output vectors are confirmed,and the MATLAB7.0-based RBF neural network passenger volume prediction model of railway passenger volume is set up.Through the actual passenger volume data of Dalian Railway Station,the prediction result is proved to be more accurate and precise in fitting and prediction precision.