关键词: 交通运输工程, 港口, 货物吞吐量, Elman神经网络, 预测, 动态学习

Abstract: To improve the prediction accuracy of port cargo throughput and further provide data support for port construction, Elman neural network with the capability of dealing with dynamic information was introduced. Elman neural network was applied to the forecast of cargo throughput in Ningbo-Zhoushan port, and the time series data were constructed by using the first six months data to recursively predict the next month data. Meanwhile, the prediction results were compared with those of BP neural network and RBF neural network. The results show that: in terms of port cargo throughput forecast, compared with BP neural network and RBF neural network, Elman neural network can better adapt to the characteristics of the throughput data varying with time, and its prediction values are closer to the actual values, whose prediction performance is better. And it can better reflect the actual state of the port.

Key words: traffic and transportation engineering, port, cargo throughput, Elman neural networks, forecasting, dynamic learning