一种改进的神经网络预测模型在铁路春运客流 关键字： 铁路客运量；预测；时间序列；神经网络[gap=646]Key words: railway passenger volume; prediction; time series; neural network
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A new prediction approach for railway passenger volume is put forward by means of Least Squares Support Vector Machine (LS-SVM).
提出了一种基于最小二乘支持向量机(LS - SVM)的铁路客运量预测的新方法。
The share of railway passenger transport market shows continuous decrease status, which caused by long-period lack of transport capacity and down railway passenger traffic volume.
我国铁路长期以来运能短缺，铁路客运量增长受到制约，导致铁路客运市场份额呈现持续下降的态势。
The segment fuzzy BP Neural Network is adopted to predict the passenger traffic volume of railways in data mining based on analyzing the data feature of railway passenger tickets.
在分析铁路客票数据特征的基础上，提出采用分段模糊BP神经网络对铁路客运量进行数据挖掘预测。
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