| 摘 要: 精确的港口货物吞吐量预测对于港口的发展至关重要。本文提出了改进粒子群优化去尾均值多层感知机模型对上海港货物吞吐量进行预测。选取了影响上海港货物吞吐量的十个因素进行训练，实验结果表明该预测模型的预测性能明显优于传统MLP预测模型和基本的粒子群优化多层感知机模型。对该预测模型的误差分析和收敛性分析表明该预测模型可靠。 |
| 关键词: 粒子群算法；去尾均值；多层感知机；港口吞吐量预测 |
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中图分类号: TP183
文献标识码: A
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| Research on Port Throughput Forecast based on Improved Multilayer Perceptron Model |
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LIU Miaomiao, JIANG Yan
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(Business School, University of Shanghai for Science and Technology, Shanghai 200093, China )
192560916@st.usst.edu.cn; ppjyan@163.com
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| Abstract: Accurate port cargo throughput forecast is vital to port development. This paper proposes an improved particle swarm optimization model of multilayer perceptron (MLP) with trimmed mean to predict the cargo throughput of Shanghai Port. Ten factors that affect cargo throughput of Shanghai Port are selected for training. The experimental results show that prediction performance of the proposed prediction model is significantly better than traditional MLP prediction model and basic particle swarm optimization multi-layer perceptron model. Error and convergence analyses of the prediction model show that the prediction model is reliable. |
| Keywords: particle swarm algorithm; trimmed mean; multilayer perceptron; port throughput forecast |
