船舶网络流量预测的灰色模型  

Grey model of ship network traffic prediction

作　　者：吴蓉蓉[1] WU Rong-rong(Jiangsu Maritime Institute,Nanjing 211170,China)

机构地区：[1]江苏海事职业技术学院,江苏南京211170

出　　处：《舰船科学技术》2022年第12期149-152,共4页Ship Science and Technology

基　　金：江苏省社科联“江苏海事智慧航运研究协同创新基地”资助项目。

摘　　要：研究船舶网络流量预测的灰色模型,精准有效地预测船舶网络流量趋势,保障船舶网络的稳定通信。采集船舶网络流量初始数据,通过小波变换Mallat算法分解重构处理此类数据,获得平滑高质量船舶网络流量数据,运用灰色模型与反向传播神经网络构建灰色预测模型,向该模型内输入处理后平滑流量数据,输出船舶网络流量预测值,实现船舶网络流量预测。结果表明,该模型处理所采集船舶网络流量数据毛刺的效果显著,处理后的船舶网络流量数据平滑性高。最终预测的船舶网络流量数据几乎与样本数据吻合,预测结果的拟合效果好、偏离度较低,整体预测精度较高,可为船舶无线网络有效避免拥堵与保持稳定通信提供保障。The grey model of ship network traffic prediction is studied to accurately and effectively predict the trend of ship network traffic and ensure the stable communication of ship network.The initial data collection vessel network traffic by Mallat algorithm of wavelet transform decomposition reconstruction process such data,to obtain smooth quality shipping network traffic data,using the grey model and the back propagation neural network to build the grey forecasting model,to the input processing within the model after smooth traffic data,output of ship network traffic prediction,to realize the network traffic prediction of the ship.The results show that the model can deal with the burrs of the collected ship network traffic data effectively,and the processed ship network traffic data has a high smoothness.Finally,the predicted ship network traffic data is almost consistent with the sample data,and the prediction results have good fitting effect,low deviation degree and high overall prediction accuracy,which can effectively avoid congestion and maintain stable communication for the ship wireless network.

关 键 词：船舶网络流量 灰色模型 小波变换 MALLAT算法 分解重构处理 反向传播网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]