基于灰色神经网络的港口货物吞吐量预测算法

摘要

通过对港口货物吞吐量影响因素的分析,运用了灰色模型理论选取部分已知信息计算未知信息,结合了人工神经网络系统对模型进行优化,给出了基于灰色神经网络的港口货物吞吐量预测算法,通过计算机仿真实验证明,并将港口货物吞吐量的实际值和预测值进行比较与误差分析,能够较好的进行港口货物吞吐量的预测,也适用于其他方面的预测分析,具有一定的实际应用价值.%In this paper, based on an analysis of the factors influencing the cargo throughput of a harbor, we used the gray model theory to select certain known information to calculate the unknown information, then applied the artificial neural network system to optimize the model, and put forward the harbor cargo throughput prediction algorithm based on the gray neural network. Next by a computer simulation test, we compared the difference between the actual and the predicted values of the cargo throughput of a harbor, showing that the algorithm could satisfactorily predict the cargo throughput of the harbor and was also applicable in other prediction applications.