基于NLAPSO．RBF的铁路货运量预测研究

铁路货运量需求预测在国家和区域经济发展规划、运输经营决策中具有重要作用。针对提高预测准确性与收敛速度问题，建立了基于RBF神经网络的预测模型。该模型具有最佳函数逼近性能和全局最优特性，适于预测计算，但有参数确定与优化的难题。提出一种基于非线性学习因子调节的粒子群优化（NLA-PSO）算法应用于RBF神经网络的参数优化，进而提高铁路货运量预测的精度与效率。通过1992--2011年铁路货运量预测的实例验证，将仿真结果与其他算法进行了比对，证明了方法的预测精度与收敛速度均优于其他算法，在铁路货运量预测计算上有效可行。

Research of railway freight volume prediction based on NLA-PSO-RBF

Railway freight volume demand prediction plays a significant role in national and regional economic development planning and transportation management decision making. In order to improve the prediction accuracy and convergence rate, a kind of prediction model which is based on RBF neural network is established. This kind of model has optimum approximation of function performance and global optimal characteristics, which is suitable for forecasting computation, meanwhile has prob- lems about determination and optimization of parameters. A type of particle swarm optimization based on nonlinear learning fac- tors adjusting（NLA-PSO） is introduced and applied to the parameters optimization of RBF neural network, which improves ac- curacy and efficiency of railway freight volume prediction. Comparison between simulation result and other algorithms is made by using examples of railway freight volume prediction during 1992 to 2011, which proves that the applied method has better prediction accuracy and convergence rate than other algorithms and has effective and feasible function on railway freight vol- ume forecasting computation.