大连港货物流量预测研究

A Study of Shipping Cargo Flux Predicting for Dalian Port

建立了RBF神经网络—Monte Carlo货物吞吐量预测模型,利用大连港货物吞吐量的历史数据,对2006-2010年以及2020年的大连港货物吞吐量进行预测.以RBF神经网络的预测为基础,利用Monte Carlo仿真方法对大连港货物吞吐量的预测结果进行可信度分析.得到了预测结果区间以及预测结果在每个区间上出现的概率.文章证明了神经网络-Monte Carlo预测模型在吞吐量预测领域中的可行性,而且预测结果为大连建设东北亚航运中心提供了参考依据.

The author＇s task in this thesis is to build RBF Network - Monte Carlo cargo throughput predicting model, predicting cargo throughput for Dalian port from 2006 to 2010, plus 2020 single year, based on historical data. In addition, another task in this thesis is to analyze the credit of predicting result by using Monte Carlo, based on RBF Network predicting algorithm. Finally, the probabilities of predicting result in every result extent have been gained. The feasibility of RBF Network - Monte Carlo cargo throughput predicting model is verified in this thesis and the resuit of prediction can be used as references for Dalian Northeast Asia shipping center construction.