基于粗神经网络的港口集装箱吞吐量预测

运用粗糙集神经网络建立了广州港集装箱吞吐量的预测模型，预测了2007～2010年的集装箱吞吐量。该预测方法融合了粗糙集理论与神经网络方法具有的优点，具有很强的学习与泛化能力，非常适合处理多因素、非线性的复杂系统。预测结果对广州港的发展有较强的借鉴作用，可以为广州港未来发展提供参考。

Rough set neural network-based container throughput predict in the port

A model for predicting container throughput is developed for Guangzhou port based on rough set neural network and the throughputs of the Guangzhou Port from 2007 to 2010 are calculated. This prediction method combines the advantage of the rough set theory and neural network method, and has a strong ability to study and generalize, well suited to deal with complex system with multi-factor, non-linear. The predicted result can be used as a reference for the development of the Guangzhou Port, and provide guidance in future.