摘要
为了提高公路货运量预测的能力,应用基于结构风险最小化准则的标准支持向量回归机方法来研究公路货运量预测问题。在选择适当的参数和核函数的基础上,通过对成都公路货运量时间序列进行预测,并与人工神经网络、线性回归分析等方法进行了对比,发现该方法能获得最小的训练相对误差和测试相对误差。
To improve the forecast ability of highway freight traffic ,SVR based on structural risk minimization was applied to forecasting highway freight traffic. By selecting appropriate parameters and kernel function, the proposed approach was used for forecasting highway freight traffic of Chengdu city. Compared with artificial neural network (ANN) and linear regression analysis, experimental results show that the training relative error and testing relative error obtained by SVR is lower than that by ANN and linear regression analysis.
出处
《计算机应用研究》
CSCD
北大核心
2008年第2期632-633,636,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(60674007)
关键词
公路货运量
支持向量回归机
人工神经网络
预测
highway freight traffic
support vector regression (SVR)
artificial neural network(ANN)
forecast