改进粒子群算法支持向量机的烟台港货物吞吐量预测

Cargo Throughput Prediction of Yantai Port Based on Improved Particle Warm Optimization Support Vector Machine

为了精准预测港口货物吞吐量,文章研究了改进粒子群算法支持向量机模型。引入自适应惯性权重以及最优粒子扰动决策,来优化支持向量机中的惩罚参数C和核函数参数g。基于1978—2020年烟台港吐吞量进行实证分析,对比改进支持向量机模型和原本支持向量机模型的平均相对误差,预测效果优于SVM模型,因此为港口货物吞吐量提供技术支持。

In order to accurately predict the cargo throughput of a port,this paper studies the improved Particle Swarm Optimization(PSO)support vector machine model.Adaptive Inertia Weight and optimal particle perturbation decision are introduced to optimize the penalty parameter C and kernel function parameter g in the support vector machine.This paper makes an empirical analysis based on Yantai Port throughput from 1978 to 2020,comparing the average relative error between the improved support vector machine model and the original support vector machine model,the forecasting effect is better than that of SVM model,therefore it provides the technical support for the port cargo throughput.