Application of grey BP neural network in port logistics demand analysis

W. Xu, Nan Yu
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引用次数: 1

Abstract

From the perspective of port cargo throughput, this paper firstly analyses the characteristics and influencing factors of port logistics demand. Secondly, considering the characteristics of nonlinear logistics demand and small sample modelling, the modelling adopts GM(1, 1) and the single prediction model of BP neural network for calculation. Then, based on the prediction results and the target of minimum fitting prediction square-error, the single model is given weight, and the combined prediction model is constructed. Finally, taken Qingdao Port as an example, the port logistics demand is simulated by MATLAB software. The results show that the combined forecasting model has higher accuracy and stronger stability than the single forecasting model, which can effectively reduce the error rate and make the forecasting result closer to reality, thus having guiding significance for the future port logistics development planning.
灰色BP神经网络在港口物流需求分析中的应用
本文首先从港口货物吞吐量的角度,分析了港口物流需求的特征及其影响因素。其次,考虑到物流需求非线性和小样本建模的特点,建模采用GM(1,1)和BP神经网络的单一预测模型进行计算。然后,根据预测结果和拟合预测平方误差最小的目标,对单个模型赋予权重,构建组合预测模型;最后,以青岛港为例,利用MATLAB软件对港口物流需求进行仿真。结果表明,组合预测模型比单一预测模型具有更高的准确性和更强的稳定性,可以有效降低错误率,使预测结果更接近实际,对未来港口物流发展规划具有指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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