基于蚁群算法优化反向传播神经网络的港口吞吐量预测  

Throughput Prediction of Port Based on Back Propagation Neural Network Optimized by Ant Colony Algorithm

作　　者：李长安[1,3,4] 卢雪琴[2] 吴忠强 张立杰 LI Chang-an;LU Xue-qin;WU Zhong-qiang;ZHANG Li-jie(Key Laboratory of Advanced Forging&Stamping Technology and Science of Ministry of Education of China,Yanshan University,Qinhuangdao,Hebei 066004,China;College of Electric Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China;Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control,Yanshan University,Qinhuangdao,Hebei 066004,China;Shenhua Tianjin Coal Terminal Co.Ltd.,Tianjin 300457,China)

机构地区：[1]燕山大学先进锻压成形技术与科学教育部重点实验室,河北秦皇岛066004 [2]燕山大学电气工程学院,河北秦皇岛066004 [3]燕山大学河北省重型机械流体动力传输与控制重点实验室,河北秦皇岛066004 [4]神华天津煤炭码头有限责任公司,天津300457

出　　处：《计量学报》2020年第11期1398-1403,共6页Acta Metrologica Sinica

摘　　要：利用蚁群算法优化反向传播神经网络的初始权值、阈值,建立预测模型,对港口货物吞吐量进行预测。蚁群算法具有全局搜索能力,分布式计算和鲁棒性强等特点,有利于加快反向传播神经网络的收敛速度,避免易陷入局部极值的问题,提高建模精度。在港口吞吐量预测中的应用表明:蚁群算法优化BP神经网络模型、模糊神经网络预测模型、RBF预测模型及BP预测模型的平均绝对百分比误差分别为2.826%、3.734%、4.990%和6.566%;同时,蚁群算法优化BP神经网络模型收敛速度最快。Port cargo throughput is an important index of port production and operation scale,and it is the basis for port construction and development.In order to maximize the role of port,it is necessary to make a reasonable and effective forecast for port cargo throughput.Ant colony algorithm is used to optimize the initial weight and threshold of BP neural network,and the prediction model is established to predict the port cargo throughput.Ant colony algorithm has the characteristics of global search,distributed computation and strong robustness,which is beneficial to accelerate the convergence speed of BP neural network,avoids the problem of easy to fall into local extremum,and improves the modeling accuracy.The application in port throughput prediction shows that the average absolute percentage errors of BP neural network model optimized by ant colony algorithm,fuzzy neural network prediction model,RBF prediction model and BP prediction model are 2.826%,3.734%,4.990%and 6.566%respectively;meanwhile,the convergence speed of BP neural network model optimized by ant colony algorithm is the fastest.

关 键 词：计量学 港口吞吐量 蚁群算法 BP神经网络 AC-BP预测模型 

分 类 号：TB938.1[一般工业技术—计量学]