CAJ | 학술논문

为更精确地对港口或航道内船舶交通流量进行预测,分别建立BP神经网络预测模型和RBF神经网络预测模型进行仿真,并以宁波港船舶交通流量为例进行验证.结果表明,在宁波港现有发展基础和港口设施状况下,RBF神经网络用于宁波港船舶交通流量预测误差较小,预测值与实际值相近.
위경정학지대항구혹항도내선박교통류량진행예측,분별건립BP신경망락예측모형화RBF신경망락예측모형진행방진,병이저파항선박교통류량위례진행험증.결과표명,재저파항현유발전기출화항구설시상황하,RBF신경망락용우저파항선박교통류량예측오차교소,예측치여실제치상근.
To forecast ship traffic volume in port or waterway precisely, back propagation (BP) and radial basis function (RBF) neural network forecasting models were established and simulated. Tests in Ningbo port show that RBF neural network has the smaller error in forecasting ship traffic volume, and the predictive value is close to actual one under the current basis and port facilities.