基于径向基神经网络的集装箱量预测模型

A study and simulation on container flow forecasting modelbased on radial basis function neural networks

提出了一种利用RBF神经网络算法对港口集装箱量进行预测的模型,并应用此模型对上海港集装箱运量进行了仿真及示例分析,同时与BP神经网络预测方法进行了比较.结果表明,运用RBF神经网络进行预测,具有较快的运算速度和较高的精度,并有很好的预测能力和应用价值.

In this paper,based on a radial basis function neural network ,a forecasting model for container flow was put forward. Both the models on RBF and BP are applied to the prediction of the container flow of ShangHai harbor. The simulation results confirm the superior performance of the RBF over the BP, and the former has been testified to be available, accurate and efficient.